#VariationalQuantumEigensolvers

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Kvantify Chemistry QDK: Quantum Chemistry Meet IQM Devices

Kvantify Chemistry QDK

The Kvantify Chemistry Quantum Development Kit (QDK) enables precise and affordable quantum chemistry computations on hybrid quantum-classical systems, advancing computational molecular modelling. Kvantify researchers' QDK works perfectly with quantum hardware, especially the IQM Resonance Cloud's 20-qubit Garnet and 16-qubit Sirius quantum processors.

The Kvantify Chemistry QDK aims to overcome the bottleneck of exact quantum simulation in the 20+ qubit zone and hardware noise in quantum software for chemical applications. It uses quantum machinery for some computations to scale quantum-chemistry calculations on larger hardware and avoid excessive classical compute requirements. Importantly, the QDK allows computational chemists to use quantum hardware without quantum algorithm knowledge, enabling general application of complex quantum chemical computations on quantum hardware.

Kvantify Chemistry QDK features:

Previous research replication and enhancement: The Kvantify Chemistry QDK replicated and refined a 2023 IBM Quantum study on butyronitrile dissociation. IBM's initial experiment used ADAPT-VQE on eight hardware qubits and a minimum basis set (STO-3G). QDK increased this by:

Instead of STO-3G, use PCSEG-2, a more realistic basis set.

Even-handed subsystem selection ensures consistent orbital selection during projective-embedding computations to increase computational model precision and reliability.

Scalability and Efficiency: The QDK calculates with up to 20 spin orbitals, employing the entire IQM Garnet quantum chip, to demonstrate its capabilities. This shows its ability to perform exact real-world chemistry computations using large quantum devices with an acceptable number of quantum-hardware operations and little classical computing.

The Kvantify Chemistry QDK relies on Kvantify's patented FAST-VQE solution. Unlike the IBM work, which only used a quantum computer for the final energy evaluation, the Kvantify QDK leverages quantum hardware (IQM Garnet or Sirius) for circuit sampling to do adaptive operator selection. Kvantify's chemistry-optimized state-vector simulator models Variational Quantum Eigensolvers (VQEs), which require many shots for energy evaluation in ADAPT-VQE. Quantum technology is ideal for low-shot-count, high-error-resilience tasks, which improves this strategic division of labour.

Accuracy and Robustness: Kvantify's exact chemistry-specific state-vector simulator and IQM's Sirius and Garnet devices agreed on correct results. Potential energy surface (PES) study shows that simulation FAST curves closely approximate CASCI (Complete Active Space Configuration Interaction) curves, indicating good accuracy. The dissociation limit has the biggest errors, as expected since the Hartree-Fock state deviates from the real state in this area.

The QDK provides computational chemists with an affordable and feasible means to conduct quantum experiments on hybrid quantum-classical systems, without the need for quantum algorithm or technology expertise.

Simulation of Butyronitrile Dissociation

Butyronitrile dissociation simulations demonstrate the QDK's potency. Butyronitrile is a fascinating chemical with practical and academic uses. The electrolyte works in DSSCs and lithium-ion batteries. Global energy change requires these technologies.

Electrolytes are severely constrained in several ways:

At lower temperatures, DSSC liquid electrolytes freeze and are viscous, but at higher temperatures, they work well. Over 60°C, volatile electrolytes may evaporate or degrade.

Similar electrolyte restrictions limit ion mobility and battery performance in low-temperature lithium-ion batteries. High-voltage cathode electrolyte breakdown lowers battery efficiency and lifespan.

Butyronitrile has the potential to solve these issues due to its low viscosity at low temperatures and chemical stability against cathode oxidation. These properties make it a strong candidate to improve upcoming energy conversion and storage technologies' performance and reliability. Quantum chemistry simulations are needed to understand its dissociation and improve butyronitrile-based electrolytes for specific applications.

The system energy was calculated throughout dissociation simulation in 2023, which required a “tremendous effort”. With the Kvantify Chemistry QDK, this study may be “effortlessly replicated using a manageable budget while obtaining accurate results”. The butyronitrile investigation used more spin orbitals, which exhausted IQM quantum processors and showed a clear relationship between computational error and molecular system complexity. The simulations were accurate, with FAST curves nearly matching CASCI curves despite spin orbitals increasing processing needs and error potential.

In conclusion

The Kvantify Chemistry QDK makes complex quantum chemical computations practical and feasible. Its simulation of butyronitrile dissociation using scalable quantum technology reveals better precision and efficiency, which could help create cutting-edge energy storage and conversion technologies. As part of the "Quantum Zeitgeist," quantum computers are being used to solve previously unsolvable problems in artificial intelligence, finance, and material science

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What is Variational Quantum Eigensolver VQE, How VQE Works

Variable Quantum Eigensolver

Hybrid Quantum Algorithm Revolutionises Simulation and Optimisation with VQE

Quantum computing has long been touted as a game-changer for solving complicated issues normal supercomputers cannot. The hybrid quantum-classical method Variational Quantum Eigensolver (VQE) is a prominent option for near-term quantum hardware. Chemistry, physics, and optimisation are changing because they can forecast molecular and material system ground state energies.

Variational Quantum Eigensolver?

In the early days of quantum computing, VQE was developed to determine a quantum system's ground state, or lowest energy state. Many scientific fields, including chemistry, require the ground state for stability and molecular structure. The hybrid VQE approach combines classical and quantum computing advantages:

Ansatz, or parametric quantum circuit, prepares candidate quantum states.

A classical optimiser adjusts this circuit's settings to reduce energy estimates based on measurements.

Shallow quantum circuits can approximate the ground state of molecular and material systems, making this technology excellent for early-stage, noisy quantum devices like NISQ processors. How VQE Works

Iterative feedback loops are crucial to VQE:

State Preparation: A parameterised quantum circuit encodes an estimated system wavefunction.

Measuring the system's energy involves measuring its Hamiltonian, an operator representing total energy.

Optimisation: Classical optimisation methods like gradient descent modify ansatz parameters after processing measurement data.

The algorithm iterates until it finds a set of parameters that yield the ground state's lowest estimated energy.

This hybrid technique allows quantum processors with few qubits to solve problems formerly reserved for computational chemists and theoretical physicists by avoiding noisy hardware.

Relevance in Quantum Computing

VQE is a critical technology for making quantum computing practicable in its early phases. It matters in several fields:

For material design and drug development, quantum chemistry delivers accurate molecular simulations and structure and reactivity data.

Design simulation of superconductor ground state energies.

Optimisation difficulties: The VQE framework can address combinatorial optimisation difficulties, making it suited for supply chain, logistics, and finance optimisation.

The approach is effective because it can be developed incrementally and uses current hardware. Even as hardware improves, researchers can easily migrate from noisy to error-corrected hardware platforms to VQE.

The VQE Evolution

Due to its usefulness and hardware-friendliness, the 2014 Variable Quantum Eigensolver algorithm became popular in quantum computing. Since then, researchers have recommended various improvements:

Ansatz Development: New ansätze improve state preparation accuracy and hardware efficiency.

Noise Mitigation: Measurement error correction and extrapolation improve noisy gear findings.

Advanced Optimisers: Traditional Optimisation approaches have improved VQE's ability to find global minima and avoid local minima.

Issues and Limitations

Despite its potential, VQE faces challenges:

Ansatz Design: Complex molecular systems make it challenging to develop an efficient state parameterisation (ansatz).

Barren Plateaus: Optimisation landscapes often have plateaus with little gradient information, making global minima difficult to find with typical optimisers.

Gate failures, decoherence, and qubit connectivity limit VQE accuracy and scalability.

Classical Optimisation Cost: Larger problems may require computationally expensive classical optimisation.

In conclusion

Early quantum computing approaches like the Variational Quantum Eigensolver can answer key scientific and practical problems. Its new hybrid method allows researchers to approximate ground-state energies using quantum hardware, making it essential in quantum chemistry, material design, and optimisation.

Due to error mitigation, ansätze design, and optimisation advances, VQE is becoming more realistic for real-world applications, notwithstanding hardware restrictions and optimisation challenges. The Variational Quantum Eigensolver will influence computation, chemistry, and other fields as large-scale, fault-tolerant quantum computing becomes possible.

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Q-AIM: Open Source Infrastructure for Quantum Computing

Q-AIM Quantum Access Infrastructure Management

Open-source Q-AIM for  quantum computing infrastructure, management, and access.

The open-source, vendor-independent platform Q-AIM (Quantum Access Infrastructure Management) makes quantum computing hardware easier to buy, meeting this critical demand. It aims to ease quantum hardware procurement and use.

Important Q-AIM aspects discussed in the article:

Design and Execution Q-AIM may be installed on cloud servers and personal devices in a portable and scalable manner due to its dockerized micro-service design. This design prioritises portability, personalisation, and resource efficiency. Reduced memory footprint facilitates seamless scalability, making Q-AIM ideal for smaller server instances at cheaper cost. Dockerization bundles software for consistent performance across contexts.

Technology Q-AIM's powerful software design uses Docker and Kubernetes for containerisation and orchestration for scalability and resource control. Google Cloud and Kubernetes can automatically launch, scale, and manage containerised apps. Simple Node.js, Angular, and Nginx interfaces enable quantum gadget interaction. Version control systems like Git simplify code maintenance and collaboration. Container monitoring systems like Cadvisor monitor resource usage to ensure peak performance.

Benefits, Function Research teams can reduce technical duplication and operational costs with Q-AIM. It streamlines complex interactions and provides a common interface for communicating with the hardware infrastructure regardless of quantum computing system. The system reduces the operational burden of maintaining and integrating quantum hardware resources by merging access and administration, allowing researchers to focus on scientific discovery.

Priorities for Application and Research The Variational Quantum Eigensolver (VQE) algorithm is studied to demonstrate how Q-AIM simplifies hardware access for complex quantum calculations. In quantum chemistry and materials research, VQE is an essential quantum computation algorithm that approximates a molecule or material's ground state energy. Q-AIM researchers can focus on algorithm development rather than hardware integration.

Other Features QASM, a human-readable quantum circuit description language, was parsed by researchers. This simplifies algorithm translation into hardware executable instructions and quantum circuit manipulation. The project also understands that quantum computing errors are common and invests in scalable error mitigation measures to ensure accuracy and reliability. Per Google Cloud computing instance prices, the methodology considers cloud deployment costs to maximise cost-effectiveness and affect design decisions.

Q-AIM helps research teams and universities buy, run, and scale quantum computing resources, accelerating progress. Future research should improve resource allocation, job scheduling, and framework interoperability with more quantum hardware.

To conclude

The majority of the publications cover quantum computing, with a focus on Q-AIM (Quantum Access Infrastructure Management), an open-source software framework for managing and accessing quantum hardware. Q-AIM uses a dockerized micro-service architecture for scalable and portable deployment to reduce researcher costs and complexity.

Quantum algorithms like Variational Quantum Eigensolver (VQE) are highlighted, but the sources also address quantum machine learning, the quantum internet, and other topics. A unified and adaptable software architecture is needed to fully use quantum technology, according to the study.

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Quantum Portfolio Optimizer: Global Data Quantum, IBM Qiskit

Portfolio optimisation for quantum computing

Global Data Quantum introduced the Quantum Portfolio Optimiser function in IBM Qiskit. Quantum computing optimises investment portfolios.

A detailed breakdown:

Quantum Portfolio Optimiser Goal

The Quantum Portfolio Optimiser optimises investment performance while reducing transaction costs and risks. Its dynamic portfolio optimisation goal is to find the optimum investment plan across many time periods to maximise projected return and minimise risks, often while considering budget, transaction costs, and risk aversion. Dynamic portfolio optimisation modifies assets based on asset performance, unlike traditional portfolio optimisation, which uses a single rebalancing time. The program targets analysts, investors, and portfolio managers. Portfolio optimisation allows backtesting trading approaches.

Quantum Portfolio Optimiser Access:

Discover the function in IBM Qiskit Functions Catalogue. This experimental functionality is only available to IBM Quantum Premium and Flex Plan users in preview release. Request a catalogue to access Global Data Quantum.

Quantum Computing—Why?

Traditional methods become slow and inefficient as resources or limits increase. Quantum computing's capacity to analyse several variables in parallel can solve complex problems faster and more efficiently than classical solvers like CPLEX, Gurobi, and Pyscf on HPC resources.

Quantum Portfolio Optimiser Functions?

The Quantum Portfolio Optimiser has four steps:

It receives financial asset values and user-specified investing conditions.

Quantum circuits convert classical input data into a quantum-resolution problem. This requires constructing the dynamic portfolio optimisation problem using Quadratic Unconstrained Binary optimisation (QUBO) and converting it into a quantum operator (Ising Hamiltonian).

The Variational Quantum Eigensolver (VQE) algorithm is considered. The VQE was designed to determine the optimal solution-wide investment combinations. In this hybrid quantum-classical approach, the quantum circuit estimates the cost function and Differential Evolution is used for classical optimisation.

Adjusting post-processing to eliminate quantum device noise yields an optimal, trustworthy, and realistic recommendation. For optimal output, the system uses noise-aware (SQD-based) post-processing.

Formulating Problems

Portfolio optimisation uses multi-objective Quadratic Unconstrained Binary Optimisation (QUBO). The QUBO function optimises four goals:

Max out the return function (F).

Reduce investment risk (R) and transaction costs.

Respect investment limits. The QUBO function is defined as O = -F + (γ/2)R + C + ρP, where γ is the risk aversion coefficient and ρ is the constraints reinforcement coefficient (Lagrange multiplier The minimum qubit count for a problem is the number of assets (na), time periods (nt), and bit resolution (nq) used to describe the investment.

Input

This function requires several input parameters:

A dictionary of asset prices uses dates as supplementary keys. All assets must have consistent data for the same dates.

Qubo_settings: A dictionary that configures the QUBO problem with parameters like nq resolution qubits, dt time window each step, maximum investment per asset, risk aversion coefficient, transaction charge, and restriction coefficient.

Optimizer_settings (Optional): Sets up the standard optimisation technique, including primitive settings (sampler_shots, estimator_shots) and differentiation_evolution algorithm parameters (num_generations, population_size).

ansatz_settings (Optional): Select ��optimized_real_amplitudes” or “tailored” and enable multiple pass managers, dynamical decoupling, and other options to configure the quantum circuit ansatz.

Optional: QPU backend name, such as “ibm_torino.”

previous_session_id (Optional): A list of past session IDs to continue execution or retrieve data.

Apply_postprocess (Optional): True applies noise-aware SQD post-processing.

tags: An optional text list to label the experiment.

Output

Function returns two dictionaries: “result” and “metadata”.

Result: optimal optimisation outcomes, such as the optimal investment strategy over time and the lowest target cost. Investment weights are normalised by total investment.

Metadata: Metadata describes all optimisation results. It includes counts, investment pathways, objective costs, Sharpe ratios, returns, limitation violations, samples/states, and transaction costs. The session ID, asset order, QUBO matrix, and resource consumption summary are all included. Return, Sharpe ratio, restriction deviation, and least objective cost are key metadata for the best solution.

Application Function Context Qiskit

Application functions like the Quantum Portfolio Optimiser provide a comprehensive quantum pipeline by abstracting the quantum workflow. Because quantum methods use conventional classical inputs and return domain-familiar classical outputs, they can be easily integrated into present application processes without quantum computing knowledge.

Analysis of Performance and Benchmarks

The function is verified using different resolution qubit, ansatz circuit, and asset grouping configurations. Benchmarks evaluate solutions using two metrics:

Objective cost: To evaluate optimisation, the objective cost compares the cost function value to Gurobi (free version) output.

Sharpe ratio: Measures portfolio risk-adjusted return. Benchmark data shows the quantum optimiser finds viable investment plans. For a test using IBEX35 assets (Set 3, 4 time steps, 2-bit encoding, 56 qubits), the Optimised Real Amplitudes ansatz had an objective cost of -3.67 and a Sharpe ratio of 14.48, while Gurobi had 16.44 and -4.11. Comparing quantum sampling to random sampling, visual inspection shows that lower prices dominate the distribution.