#quantumcircuits

[govindhtech]

IBM Boosts Qiskit, the Leading Quantum Software Toolkit

IBM Qiskit

IBM today announced the development and global adoption of its quantum software, Qiskit. Since its launch in 2017, Qiskit, an open-source software development kit (SDK), has enabled over 550,000 users to create and execute quantum circuits on IBM’s quantum hardware platforms, totaling over 3 trillion quantum circuit executions.

To achieve even greater performance, Qiskit has been developed into a whole software stack in its most recent version. From its humble beginnings as a well-liked quantum software development kit for investigating and executing quantum computing experiments, it has developed into a reliable SDK and services portfolio, designed to help users gain better performance when executing intricate quantum circuits on more than 100 qubit IBM quantum computers.

Members of the IBM Quantum Network will be able to discover the next generation of quantum algorithms in their respective areas with the most powerful Qiskit capabilities thanks to this extension, which will also help them uncover quantum advantage.

Users must have a set of tools that can map their issues to make use of both sophisticated classical and quantum computation, optimise the problem for effective quantum execution, and then successfully execute the quantum circuits on actual quantum hardware in order to achieve quantum advantage. These tools, which IBM has been developing for the past seven years, are now coming together to form the Qiskit software stack.

Qiskit has had over 100 releases since its debut as a pioneering quantum computing research tool. Qiskit is used by enterprises, governments, research centres, and universities to undertake large-scale quantum experiments.

The Qiskit software stack is expanded to include:

The Qiskit SDK v1.x stable release is designed for creating, refining, and displaying quantum circuits.

Quantum circuit optimisation using artificial intelligence (AI) integrated into the Qiskit Transpiler Service.

Simplified modes of operation for the Qiskit Runtime Service, which may be adjusted to run quantum circuits efficiently on quantum hardware.

Watsonx-based generative AI models enable the Qiskit Code Assistant to automate the creation of quantum code.

Using quantum hardware and classical clusters, quantum-centric supercomputing tasks can be executed using the Qiskit Serverless open-source platform.

Qiskit SDK

Circuits for quantum hardware can now be optimized 39 times faster than with Qiskit 0.33 thanks to the addition of new features and enhancements to the Qiskit SDK. In addition, Qiskit is designed to minimize overhead and minimize the size of circuits; it has been shown to cut memory use by an average of three times when compared to Qiskit 0.43.

Additionally, by integrating AI and heuristic passes with the Qiskit Transpiler Service, customers can minimise circuit depth in comparison to utilising the Qiskit SDK without AI optimisation.

According to Jay Gambetta, IBM Fellow and Vice President, IBM Quantum, “the global adoption of quantum computing and the discovery of quantum advantage will require a combination of leading quantum hardware alongside a robust and performant software stack to run workloads.” The algorithm discovery process that has started on utility-scale quantum technology is based on these two foundations. The Qiskit stack is expected to serve as a fundamental tool for investigating the computational domains where quantum computing shines, as an expanding quantum ecosystem matches its most challenging issues to quantum circuits.

In 2023, IBM gave its quantum hardware’s utility-scale capabilities its first public demonstration. This was the first step towards a future where quantum hardware would be able to execute quantum circuits more quickly and precisely than a classical computer could emulate a quantum computer. Designed to optimise the capabilities of cutting-edge quantum hardware, the Qiskit software stack seeks to support a worldwide community of users in exploring novel quantum algorithms that investigate scenarios in which quantum computing may outperform traditional methods in solving problems.

Giorgio Cortiana, Head of Data and AI – Energy Intelligence, E.ON, stated, “it offers a valuable set of tools for E.ON as we investigate how quantum computing could help us navigate the financial and operational complexities of the energy industry.” “Our team is able to advance utility-scale prototypes with this as a performant foundation to build and discover quantum algorithms that can be applied to business use cases, with the aim of finding new solutions to challenges in the European energy sector.”

Senior scientist Stephan Eidenbenz of Los Alamos National Laboratory stated, “We started using Qiskit for our quantum computing efforts several years ago as part of an effort to help develop a quantum-ready workforce.” Every day, scientists in the lab utilise this to experiment with novel algorithmic concepts and to communicate with IBM’s quantum hardware backends. Our team can also add compiler optimisation steps and enable pulse-level access thanks to it’s open nature.

We have executed circuits on IBM’s quantum hardware at Brookhaven using Qiskit, and this work has led to the publication of around 20 articles to date, covering topics such as condensed matter systems, dynamic systems, and physics frontiers. According to James Misewich, Associate Laboratory Director for Energy and Photon Sciences at Brookhaven National Laboratory, “Qiskit has also allowed our teams to develop extensions that push forward our exploration of bosonic and hybrid qubit-bosonic circuits, and how they could advance fundamental quantum algorithm development and error correction.”

“We have integrated IBM’s Qiskit resources and tutorials into our educational programmes through Brookhaven’s Co-design Centre for Quantum Advantage, where we partner with academic institutions like Stony Brook University to prepare the quantum workforce of the future, as we advance the scientific applications of quantum computing.”

Director of the Department of Energy’s Quantum Science Centre at Oak Ridge National Laboratory, Travis Humble, stated, “Advances in quantum computing software can help support the innovation and rapid growth of our user community and their developing technologies for our Quantum Computing User Programme here at Oak Ridge National Laboratory.” Enhancements in software efficiency will have a substantial influence on how users assess and test the capabilities of current quantum computing systems.

“The Q-CTRL team is excited about collaborating with Qiskit for building,” stated Michael J. Biercuk, the company’s founder and CEO. “Its flexible new interfaces and enhanced stability are enabling us to efficiently build simple abstractions on top of our powerful performance-management software at utility scale, so end users can explore their toughest problems with a single command.”

Constructed for the Quantum Utility Era and Beyond

The breakthrough quantum circuits to advance the quantum utility era are planned to be run by it’s software stack, which is designed to handle the quickly evolving quantum hardware and offers flexibility independent of vendor. This is accomplished by using the Rust programming language in place of performance-critical code, together with an extensive set of tools to facilitate the effective operation of quantum circuits.

The company anticipates that it will continue to provide a framework for the open, iterative, and collaborative development of new quantum algorithms and applications, carried out in conjunction with a growing global ecosystem of clients across industries and domain expertise areas, as IBM continues to build milestones along its IBM Quantum Development and Innovation Roadmap towards error-corrected systems.

Furthermore, the goal of these developing capabilities is to assist users in combining classical and quantum computing resources into a new high-performance computing paradigm characterised by quantum-centric supercomputing, which combines CPUs, GPUs, and QPUs. This next step in high-performance computing, orchestrated by it’s performant software layer, intends to create significant, new, and powerful opportunities for companies throughout the world.

Qiskit 1.0

Please note that IBM’s performance claims for Qiskit are based on comparisons between the software’s performance in its present version and its performance in relevant previous versions when users could access similar functionality. The IBM Quantum Summit 2021 saw a total speed time of 430.89 seconds for Qiskit 0.33. When Qiskit 1.0 was released in February 2024, its total speed time was equal to 10.9 seconds.

Please note that IBM’s performance claims for it is based on comparisons between the software’s performance in its present version and its performance in relevant previous versions when users could access similar functionality. In May 2023, Qiskit 0.43 used 1,750 MiB of RAM. When Qiskit 1.0 was released in February 2024, its memory utilisation was equal to 580 MiB.

The plans, directions, and intentions expressed by IBM are subject to modification or retraction at any time, at IBM’s sole discretion, and without prior notice. Any future features or functionality that we mention for our products are subject to our exclusive discretion regarding their development, release, and timing.

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[codefrombasics12345]

Quantum Computing & Python: What Developers Need to Know

Quantum computing is one of the most exciting frontiers in technology, promising to revolutionize industries by solving problems that are currently impossible for classical computers. Python, being a versatile and widely used programming language, has become the go-to tool for quantum computing simulations and research. In this blog, we’ll explore the fundamentals of quantum computing, how Python fits into the ecosystem, and what developers should know to get started.

Understanding Quantum Computing Basics

Unlike classical computers that use bits (0s and 1s), quantum computers use qubits, which leverage the principles of superposition and entanglement to process information in fundamentally different ways. This allows quantum computers to perform calculations exponentially faster for specific tasks like cryptography, optimization, and complex simulations.

Superposition – A qubit can exist in multiple states (0 and 1) simultaneously, enabling parallel computations.

Entanglement – Qubits can be interconnected, meaning the state of one qubit can instantly influence another, even over vast distances.

Quantum Gates – Instead of traditional logic gates, quantum computing relies on quantum gates that manipulate qubits in unique ways.

Python’s Role in Quantum Computing

Python plays a crucial role in quantum computing, providing user-friendly libraries and frameworks for quantum programming. Here are some of the most popular quantum computing libraries in Python:

Qiskit (by IBM) – A powerful open-source framework for working with quantum computers and simulators.

Cirq (by Google) – Focuses on building, simulating, and running quantum circuits.

PennyLane (by Xanadu) – An advanced library for quantum machine learning.

QuTiP – Designed for quantum physics simulations.

Getting Started with Quantum Computing in Python

If you’re a Python developer looking to explore quantum computing, follow these steps:

Install Qiskit – pip install qiskit

Create a Simple Quantum Circuit

from qiskit import QuantumCircuit circuit = QuantumCircuit(2) circuit.h(0) # Apply Hadamard gate circuit.cx(0, 1) # Apply CNOT gate print(circuit)

Run on a Simulator or Real Quantum Computer – IBM provides free cloud access to quantum computers via IBM Quantum Experience.

Future of Quantum Computing

Quantum computing is still in its early stages, but its potential impact is vast. Fields like cryptography, AI, drug discovery, and climate modeling stand to benefit immensely. As quantum hardware advances, Python developers equipped with quantum programming skills will be in high demand.

Conclusion

Quantum computing is no longer just theoretical—it’s becoming an essential technology for the future. Python developers interested in innovation should start exploring quantum programming today. With tools like Qiskit and Cirq, diving into quantum computing has never been easier. Stay ahead of the curve and be part of the quantum revolution!

Python Training In Chennai

Python Course In Chennai

[scarlettjohanssonnf]

Creating an oscillating photonic Bell state from a semiconductor quantum dot for quantum key distribution requires a comprehensive understanding of quantum mechanics and quantum information theory. While I can provide a high-level overview of the process, implementing it in Python would require a significant amount of code and expertise in quantum computing libraries like Qiskit or Cirq. Here’s a simplified outline of the process: Quantum Dot Initialization: Initialize the semiconductor quantum dot in a specific quantum state. Photon Emission: Stimulate the quantum dot to emit photons. The emitted photons should be entangled due to the quantum properties of the quantum dot. Photon Detection: Detect the emitted photons using photodetectors. Quantum Operations: Apply quantum operations (e.g., Bell state measurement) on the detected photons to extract the key information. Error Correction and Privacy Amplification: Implement error correction and privacy amplification protocols to ensure the security and integrity of the generated key. Below is a conceptual code snippet using Qiskit to demonstrate the creation of a Bell state: from qiskit import QuantumCircuit, Aer, execute # Create a quantum circuit with two qubits qc = QuantumCircuit(2, 2) # Apply Hadamard gate to the first qubit qc.h(0) # Apply CNOT gate with the first qubit as control and the second qubit as target qc.cx(0, 1) # Measure both qubits qc.measure([0,1], [0,1]) # Simulate the circuit simulator = Aer.get_backend(‘qasm_simulator’) result = execute(qc, simulator, shots=1000).result() # Get the counts counts = result.get_counts(qc) print(counts) This code creates a Bell state (an entangled state) between two qubits and measures the result. However, this is a simple example and doesn’t incorporate the complexities of semiconductor quantum dots and photon emission. For a complete implementation tailored to semiconductor quantum dots and photonic Bell states, you would need to delve deeper into the specifics of your experimental setup and the quantum computing framework you’re using.

[alexanderrogge]

University of Innsbruck - Scientists develop fermionic quantum processor:

FermionicAtoms #FermionicGates #Fermion #QuantumCircuit #QuantumProcessor #NeutralAtomArray #QuantumComputing #ComputerScience #QuantumPhysics #Physics

[physics369]

Quantum Gate:---Raju Rai

We are living in mysterious world where mathematics plays the vital role in every action and reaction. So what we know is we have three realm where we can apply mathematics mostly, they are Classical, Quantum and Huge body realm. on the basis of different realm’s phenomenon technology is growing in positive or negative direction. In this article i only write about classical and quantum technologies.

* Classical Technology:

We are living the classical world and it is very beautiful. We live here we need different facilities in context of technology. So to development of those facilities we should go through different fundamental things among from Circuits gates comes first because it is very building block of technology. In classical devices we use the gates, and they are:

1) Basic gates(OR, AND,NOT)

2) Universal gates( NAND, NOR)

3) Special gates(X-OR ,X-NOR)

OR gate is basic gate of electronics devices, which gives high output when there should be at least one high input. otherwise output will be low.

AND gate gives an output as 1 only when both of its input signals are 1 but gives an output as 0 when both or either of the input signals is 0.

NOT gate uses just one input to generate one output. A NOT gate inverts the input - the output is 1 (high) if the input is 0 (low),

NAND gate is an electronic logic gate that is a combination of an AND gate and a NOT gate, it is universal gate because we can make other gate on the basis of this gate. In this gate the out of will be invert of AND gate.

NOR gate is simply an OR gate followed by a NOT gate. The output is 1 only when all inputs are 0. Or the output is high when all the inputs are low. These are also called Universal gates since the earlier three gates can be realized by using the NOR gate.

X-OR gate  an abbreviation for “Exclusively-OR.” The simplest XOR gate is a two-input digital circuit that outputs a logical “1” if the two input values differ.

XNOR gate (sometimes referred to by its extended name, Exclusive NOR gate) is a digital logic gate with two or more inputs and one output that performs logical equality. The output of an XNOR gate is true when all of its inputs are true or when all of its inputs are false.

*Quantum Technology:

Now-days we trying to implement the quantum phenomenon(Super-position and Entanglement ) in many devices to get the highest form efficiency and speed. So we have quantum technology and to build that technology we need basic block of circuit that’s we called quantum gates. There are several gates:

1) Pauli X-gate

2) Pauli Y-gate

3) Pauli Z-gate

4) Hadamard gate

5) S-gate

6) T-gate

7) CNOT gate, CX gate

8) CZ gate

9) SWAP gate

10) TOFFOLI gate(CCNOT)

*Pauli-X gate:

The Pauli-X gate is a single-qubit rotation through \piπ radians around the x-axis.

CODE:

qc = QuantumCircuit(1)

qc.x(0) 

qc.draw()

* Pauli-Y gate:

The Pauli-Y gate is a single-qubit rotation through π radians around the y-axis.

CODE:

qc = QuantumCircuit(1)

qc.y(0)

qc.draw()

* Pauli Z-gate:

The Z-gate is a unitary gate that acts on only one qubit. Specifically it maps 1 to -1 and leaves 0 unchanged. It does this by rotating around the Z axis of the qubit by π radians (180 degrees). By doing this it flips the phase of the qubit.

CODE:

qc = QuantumCircuit(1)

qc.z(0)

qc.draw()

* Pauli H-gate:

H-gate is one of very important and interesting gate. which creates the super-position.   (H-gate) is a fundamental quantum gate. It allows us to move away from the poles of the Bloch sphere and create a superposition of |0⟩|0⟩ and |1⟩|1⟩

CODE:

qc = QuantumCircuit(1)

qc.h(0)

qc.draw()

* CNOT gate:

Controlled NOT gate (also C-NOT or CNOT) is a quantum logic gate that is an essential component in the construction of a gate-based quantum computer. It can be used to entangle and disentangle Bell states.

CODE:

qc = QuantumCircuit(2)

qc.cx(0,1) 

 qc.draw()

Note: For other gates, i will write next article.

THANK YOU:

[habibihojat-blog]

Recently #Google announced it had achieved a breakthrough in quantum computing.

#Google designed a #quantumprocessor named #Sycamore which consists of a two-dimensional array of 54 transmon qubits, where each qubit is tunably coupled to four nearest neighbours, in a rectangular lattice. 

Google's Sycamore processor takes about 200 seconds to sample one instance of a #quantumcircuit a million times—google’s benchmarks currently indicate that the equivalent task for a state-of-the-art classical #supercomputer would take approximately 10,000 years. This dramatic increase in speed compared to all known classical algorithms is an experimental realization of #quantumsupremacy. (Nature 574, 505–510 (2019))

 Is it a real Quantum Supremacy?! Lets discuss bout it here or everywhere u like.

Click https://www.nature.com/articles/s41586-019-1666-5#citeas to read the article.

[govindhtech]

Qoro Quantum And CESGA For Distributed Quantum Simulation

Qoro Quantum

Qoro Quantum and CESGA represent distributed quantum circuits with high-performance computing. Using Qoro Quantum's orchestration software and CESGA's CUNQA emulator, a test study showed scalable, distributed quantum circuit simulations over 10 HPC nodes. To assess distributed VQE and QAOA implementations, Qoro's Divi software built and scheduled thousands of quantum circuits for simulation on CESGA's infrastructure.

VQE and QAOA workloads finished in less than a second, demonstrating that high-throughput quantum algorithm simulations may be done with little code and efficient resources.

The pilot proved that distributed emulators like CUNQA can prepare HPC systems for large-scale quantum computing deployments by validating hybrid quantum-classical operations.

A pilot research from the Galician Supercomputing Centre (CESGA) and Qoro Quantum reveals how high-performance computing platforms may facilitate scalable, distributed quantum circuit simulations. A Qoro Quantum release said the two-week collaboration involved implementing Qoro's middleware orchestration platform to execute distributed versions of the variational quantum eigensolver and quantum approximate optimisation algorithm across CESGA's QMIO infrastructure.

Quantum Workload Integration and HPC Systems

Qoro's Divi quantum application layer automates hybrid quantum-classical algorithm orchestration and parallelisation. Divi created and ran quantum workloads on 10 HPC nodes using CESGA's CUNQA distributed QPU simulation framework for the pilot.

The announcement states that CESGA's modular testbed CUNQA mimics distributed QPU settings with customisable topologies and noise models. Qoro's technology might simulate quantum workloads in a multi-node setup to meet the demands of emerging hybrid quantum-HPC systems.

Everything worked perfectly, communication went well, and end-to-end functionality worked as intended.

Comparing QAOA and VQE in Distributed HPC

The variational hybrid approach VQE is used to estimate the ground-state energy of quantum systems, a major problem in quantum chemistry. Qoro and CESGA modelled a hydrogen molecule using two ansätze Hartree-Fock and Unitary Coupled Cluster Singles and Doubles in this pilot. Divi made 6,000 VQE circuits based on 20 bond length values.

With 10 computational nodes, the CUNQA emulator investigated the ansatz parameter space via Monte Carlo optimisation. Qoro says it replicated full demand in 0.51 seconds. Data collected automatically and returned for analysis show that the platform can enable high-throughput testing with only 15 lines of Divi code.

The researchers also evaluated QAOA, a quantum-classical technique for Max-Cut and combinatorial optimisation. This data clustering, circuit design, and logistics challenge involves partitioning a graph to maximise edges between two subgroups.

A 150-node network was partitioned into 15 clusters for simulation, and Qoro's Divi software built Monte Carlo parameterised circuits.Tests included 21,375 circuits in 15.44 seconds and 2,850 circuits in 2.13 seconds. The quantum-classical cut size ratio grew from 0.51 to 0.65 with sample size. The CUNQA emulator ran all circuits in parallel again utilising CESGA's architecture.

Performance, Infrastructure, and Prospects

Several pilot research results demonstrate scalable hybrid quantum computing advances. According to the Qoro Quantum release, Qoro's orchestration platform and CESGA's distributed quantum emulator provided faultless communication between the simulated QPU infrastructure and application layer. The cooperation also demonstrated how Qoro's Divi software could automatically generate and plan enormous quantum workloads, simplifying complex quantum applications.

The experiment also shown that distributed execution of hybrid quantum-classical algorithms over several HPC nodes may enhance performance without much human setup. Finally, the pilot showed key technological elements for scaling quantum workloads in high-performance computing. These insights will inform future distributed quantum system design.

Simulating distributed quantum architectures shows how HPC infrastructure might manage future quantum workloads. Qoro Quantum and CESGA plan to improve this method to enable quantum computing in large classical contexts.

CUNQA is being established as part of Quantum Spain with EU and Spanish Ministry for Digital Transformation support. ERDF_REACT EU funded this project's QMIO infrastructure for COVID-19 response.

[govindhtech]

Quantum Reservoir Computing QRC For Soft Robot Control

Use quantum reservoir computing to explore quantum machine learning's frontiers. Keio University and Mitsubishi Chemical used QRC to study and forecast flexible soft robot behaviour.

Quantum Innovation Centres IBM

In 2017, Keio University became one of the first IBM Quantum Hubs, now QICs. There are about 40 QICs globally. QICs use IBM Quantum's expertise to advance quantum computing. These global hubs promote a worldwide quantum ecosystem, creative quantum research, and quantum learning communities by drawing participants to joint research initiatives.

Keio University works with leading Japanese companies to develop quantum applications and algorithms as a QIC. The university's partnership with Mitsubishi Chemical, a global materials science research and development leader, is an example. In 2023, scholars from the two organisations, the University of Tokyo, the University of Arizona, and the University of New South Wales conducted a utility-scale experiment utilising an IBM Quantum device to execute a proposed quantum reservoir computing technology. This investigation established a thriving research endeavour.

Reservoir computing with utility-scale quantum computation

Reservoir computing (RC) reduces the training overhead of neural networks and generative adversarial networks. A reservoir is a computing resource that can conduct mathematical transformations on incoming system data, allowing large datasets to be manipulated while keeping data point connections.

Researchers send input system data to the reservoir in a reservoir computing experiment. Researchers will use post-processing to find answers in the reservoir's changed data. This post-processing often uses the linear regression model, an ML model for variable relationships. After training a linear regression model using reservoir output data, researchers can construct a time series that predicts input system behaviour.

Quantum reservoir computing (QRC) uses quantum computers as its reservoir. Quantum computers, which may surpass standard systems in computing capability, are ideal for high-dimensional data processing.

Mitsubishi Chemical, Keio University, and others are studying how quantum reservoir computing might help comprehend complicated natural systems. Their 2023 experiment aimed to create a quantum reservoir computing model that could predict the noisy, non-linear motions of a "soft robot," a malleable device controlled by air pressure.

Creating Quantum Reservoir Computing techniques

The researchers converted robot movement data into IBM quantum reservoir-readable quantum input states to begin the experiment. These inputs reached the reservoir. After applying random gates to input states, the reservoir produces changed signals. After that, researchers post-process output data with linear regression. The result is a robot movement prediction time series. The researchers evaluate this prediction against real data to determine its accuracy.

Most quantum reservoir computing systems measure at the end of a quantum circuit, therefore you must build up and run the system for every qubit at every time step. This can increase experiment duration and reduce time series accuracy. The Keio University and Mitsubishi Chemical research sought to overcome these limitations with “repeated measurements”.

They add qubits and measure them repeatedly instead of setting up and executing the system at each time step. This method allows researchers to collect the time series at once, resulting in a more accurate series and less circuit time.

The researchers demonstrated their quantum reservoir computing system using IBM Quantum processors with up to 120 qubits. They found that repeated measurements yielded higher accuracy and faster execution than standard QRC methods. Their first studies suggest it might accelerate calculating.

Before RC and quantum reservoir computing can solve problems, additional research is needed. The researchers say their utility-scale investigations may outperform standard modelling methods. They plan to study quantum reservoir computing for nonlinear problems like financial risk modelling.

How Quantum Innovation Centres Help Enterprise Research Organisations

Keio University and Mitsubishi Chemical's relationship is an example of how businesses may benefit from IBM Quantum Innovation Centre partnerships. Professors and students who are strong in quantum computing and at teaching other researchers in difficult issues may assist enterprise researchers achieve advanced quantum skills through these relationships.

Not just Mitsubishi Chemical, but also other global firms are benefiting from this. In addition to Mistubishi Chemical, Keio University is collaborating with corporate R&D teams from leading companies in many industries and quantum use cases to investigate exciting quantum applications and algorithm development. These collaborations show how industry research trials with universities may lead to valuable real-world applications and how QICs can help corporations explore fascinating quantum use cases.

[govindhtech]

New Advancements Of Photonic Quantum Circuits Explained

What is Photonic Quantum Computing?

Quantum computing that use photons as the physical system to do the calculation is known as photonic quantum computation. Due to their room temperature operation and the relative maturity of photonic technology, photons make perfect quantum systems.

Rapid Quantum Computing with a Novel 8-Photon Qubit Chip

In order to achieve up to 6-qubit entanglement, researchers have developed a quantum circuit device that regulates photons for sophisticated quantum computing.

Researchers from South Korea have created a novel photonic quantum circuit device that might quicken the race for quantum computing worldwide.

With the ability to manipulate up to eight photons, this device represents a major advancement in the manipulation of intricate quantum phenomena such as multipartite entanglement.

Breakthrough in Photonic Quantum Circuit Development

By creating an integrated quantum circuit device that makes use of photons, or light particles, a group of South Korean researchers has accomplished a significant milestone. Their standing in the field of quantum computing research is anticipated to improve as a result of this discovery.

A photonic integrated-circuit device that can control eight photons has been developed, according to the Electronics and Telecommunications Research Institute (ETRI). Multipartite entanglement, which results from interactions between the photons, is one of the complicated quantum phenomena that may be studied using this novel setup.

Reaching Significant Milestones in Quantum Entanglement

ETRI has achieved record performance for a 4-qubit silicon photonics device by successfully demonstrating 2-qubit and 4-qubit quantum entanglement via intensive research into silicon-photonic quantum circuits. Collaboration with KAIST and the University of Trento in Italy led to these achievements, and the results have been published in the esteemed publications APL Photonics and Photonics Research.

As a step forward, ETRI recently used a device made to manage 8-photonic qubits to show 6-qubit entanglement. Based on a silicon-photonic device, the 6-qubit entanglement is a record-breaking accomplishment in quantum states.

Promising Future for Photonic Quantum Computers

One of the most promising technologies being actively researched for the construction of a universal quantum computer is quantum circuits based on photonic qubits. A universal quantum computer might be realized by integrating several photonic qubits onto a fingernail-sized silicon chip and connecting a huge number of these tiny chips via optical fibers to create a wide network of qubits. The benefits of photonic quantum computers include low energy consumption, room-temperature operation, and scalability via optical networking.

A pair of photon propagation routes, one designated as 0 and the other as 1, may be used to encode a photonic qubit. Eight propagation pathways are needed for a circuit with four qubits, while sixteen paths are needed for a circuit with eight qubits. A photonic chip, including photon sources, optical filters, and linear-optic switches, may modify quantum states, which are then monitored by means of very sensitive single-photon detectors.

Eight photonic sources and around forty optical switches that regulate the photons’ propagation pathways are part of the 8-qubit device. Of these 40 switches, about half are particularly used as linear-optic quantum gates. By using single-photon detectors to measure the final quantum states, the configuration offers the basic structure for a quantum computer.

The Hong-Ou-Mandel effect, an intriguing quantum phenomena where two distinct photons coming from separate directions may interact and travel together along the same route, was recorded by the study team. They showed a 4-qubit entangled state on a 4-qubit integrated circuit (5mm x 5mm) in another noteworthy quantum experiment. Recently, they have extended their study to include 8-qubit integrated circuit (10mm x 5mm) tests using 8 photons. As part of their continued work toward quantum computers, the researchers want to create 16-qubit circuits this year and then scale up to 32-qubits.

Abstracts

Commercially viable quantum key distribution systems are the first quantum technology that uses quantum mechanical phenomena for its fundamental function. By encoding data in photons, this method improves security by making it possible to identify system eavesdroppers. Large-scale secure networks, improved lithography and measurement, and quantum information processors which offer tenfold more processing capacity for certain tasks are examples of anticipated future quantum technology.

Because of photons’ exceptional low-noise characteristics and high-speed transmission, photonics is destined to play a key part in these technologies. These devices will surely apply and propel cutting-edge advancements in photonics, whether they use single photons, quantum states of intense laser beams, or both.

Read more on Govindhtech.com

[scarlettjohanssonnf]

Creating a Python code reel for quantum computing telephony, qubit processing, and parallel computing is quite an intricate task. Below is a simplified example of how you might initiate a quantum telephony process using qubits and parallel computing in Python. Note that this is a conceptual example and does not represent a complete implementation: import qiskit from qiskit import QuantumCircuit, execute, Aer from qiskit.visualization import plot_histogram from multiprocessing import Pool # Function to create a quantum circuit for telephony def create_telephony_circuit(input_data): qc = QuantumCircuit(2, 2) # Encode input data onto qubit 0 qc.h(0) qc.cx(0, 1) # Teleportation protocol qc.measure([0, 1], [0, 1]) # Measure qubits return qc # Function to execute quantum circuits in parallel def execute_circuit(circuit): simulator = Aer.get_backend(‘qasm_simulator’) result = execute(circuit, simulator, shots=1024).result() counts = result.get_counts(circuit) return counts if __name__ == ‘__main__’: input_data = “Hello, world!” # Create multiple telephony circuits for parallel processing circuits = [create_telephony_circuit(input_data) for _ in range(4)] # Parallel execution of quantum circuits with Pool() as p: results = p.map(execute_circuit, circuits) # Combine and analyze results combined_counts = {} for result in results: for key, value in result.items(): combined_counts[key] = combined_counts.get(key, 0) + value # Visualize combined results print(“Combined Results:”) print(combined_counts) plot_histogram(combined_counts) This code demonstrates a basic parallel processing approach for quantum telephony using the Qiskit library for quantum computing in Python. It creates multiple quantum circuits for telephony, executes them in parallel using Python’s multiprocessing module, and then combines and visualizes the results. Remember, actual implementations may vary significantly based on the specifics of your application and the capabilities of your quantum hardware or simulator.

[scarlettjohanssonnf]

Creating a full superconductor simulator for a quantum protocol layer would be quite complex, but I can provide you with a basic Python code snippet for simulating a simple superconducting qubit. This won’t cover the entire protocol layer, but it can serve as a starting point. Here’s a basic example using Qiskit: from qiskit import QuantumCircuit, Aer, execute # Create a quantum circuit with one qubit qc = QuantumCircuit(1, 1) # Apply a Hadamard gate to create a superposition qc.h(0) # Measure the qubit qc.measure(0, 0) # Simulate the circuit simulator = Aer.get_backend(‘qasm_simulator’) job = execute(qc, simulator, shots=1000) result = job.result() # Get the counts counts = result.get_counts(qc) print(“Measurement outcome:”, counts) This code creates a simple quantum circuit with a single qubit, applies a Hadamard gate to put it in a superposition state, and then measures the qubit. Finally, it runs the circuit on a simulator and prints the measurement outcome. To run this code, you’ll need to have Qiskit installed (pip install qiskit). You can also visualize the circuit using qc.draw(). For a more complex simulation involving a quantum protocol layer, you’d need to extend this code significantly. from qiskit import QuantumCircuit, Aer, execute # Create a quantum circuit with one qubit qc = QuantumCircuit(1) # Apply a Hadamard gate to create a superposition qc.h(0) # Apply a rotation gate around the Y axis qc.ry(0.1, 0) # Apply a measurement qc.measure_all() # Draw the circuit print(qc.draw()) # Simulate the circuit simulator = Aer.get_backend(‘qasm_simulator’) job = execute(qc, simulator, shots=1024) result = job.result() # Get the counts counts = result.get_counts(qc) print(“Measurement outcome:”, counts) In this example: • We create a quantum circuit with one qubit. • We apply a Hadamard gate (qc.h(0)) to create a superposition state. • We apply a rotation gate around the Y axis (qc.ry(0.1, 0)). • We measure the qubit (qc.measure_all()). • We draw the circuit. • We simulate the circuit using a quantum simulator. • We print the measurement outcome. You can adjust the gates and measurements according to your needs. This code demonstrates the basic structure of a quantum circuit for a single qubit using Qiskit. #quantumPositionalAxial

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Quantum lattice polynomial qubit router in python

Creating a quantum lattice polynomial qubit router involves complex concepts and likely requires a quantum computing framework like Qiskit or Cirq. Here’s a simplified example using Qiskit for a basic quantum circuit:from qiskit import QuantumCircuit, Aer, transpile, assemble, execute # Create a quantum circuit with qubits arranged in a lattice num_qubits = 3 circuit = QuantumCircuit(num_qubits)…