Hardware-Efficient Variational Quantum Eigenvalue Solver
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Solution Overview
Problem
Quantum computing faces challenges in efficiently finding eigenvalues of certain operators, particularly in electronic-structure problems, due to exponential growth of problem space and sensitivity to coherence and gate errors, leading to long experiment times and high measurement requirements.
Innovation Solution
A hardware-efficient variational quantum eigenvalue solver (VQE) is implemented using a quantum computer with qubits, employing trial states parameterized by quantum gates tailored to existing hardware, entanglement, and microwave-only control to maintain coherence and reduce measurement overhead, facilitating parallelization and efficient energy estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional quantum computing approaches are used to find eigenvalues of operators, then measurement precision can be achieved, but experiment time increases exponentially and coherence requirements become extremely stringent
Solution Approach 1:
The patent changes the parameter of quantum state representation from full exponential basis to a hardware-efficient parameterized form with fewer variables. By parameterizing the quantum circuit with a limited set of rotation angles and entanglement operations tailored to the specific quantum hardware architecture, the method reduces the dimensionality of the optimization space while maintaining sufficient expressiveness to represent the ground state, thereby reducing experiment time without sacrificing measurement precision
Solution Approach 2:
The patent segments the quantum computing task into two parts: a classical optimization step that determines the parameters of the trial state, and a quantum measurement step that evaluates the energy expectation value. This segmentation allows the use of variational methods where the classical computer handles the complex optimization while the quantum computer performs only the measurement, significantly reducing the quantum experiment time required compared to traditional quantum algorithms
2Measurement precision
If traditional quantum computing approaches are used to find eigenvalues of operators, then measurement precision can be achieved, but the number of measurements required increases exponentially
Solution Approach 1:
By changing to a hardware-efficient parameterized form with fewer variational parameters, the patent reduces the number of measurements needed. The reduced parameter space means that the statistical uncertainty in energy measurements can be reduced to acceptable levels with fewer quantum circuit executions, directly addressing the exponential scaling problem of measurement requirements
3Measurement precision
If complex quantum circuits are used to achieve accurate energy estimation, then measurement precision improves, but sensitivity to coherence and gate errors increases
Solution Approach 1:
The patent applies local quality by tailoring the quantum circuit architecture to the specific properties of the quantum hardware being used. The hardware-efficient parameterized form incorporates the actual connectivity and gate sets available on the device, creating a circuit that is both accurate and resilient to the specific error characteristics of that hardware platform
Solution Approach 2:
The patent uses a dynamic variational approach where the quantum circuit parameters are continuously optimized based on measured energy values. This dynamic adjustment allows the system to adapt to noise and errors by finding parameter sets that minimize the impact of decoherence and gate errors on the final energy measurement
4Productivity
If hardware-efficient parameterized form is used, then experiment time is reduced and coherence requirements are relaxed, but device complexity must be tailored to specific hardware
Solution Approach 1:
The patent creates a universal framework for hardware-efficient quantum computing that can be applied to different quantum hardware platforms. The hardware-efficient parameterized form is designed to work with various quantum device architectures by adjusting the specific entanglement operations and connectivity patterns, providing a multi-functional solution that maintains productivity across different hardware implementations
Data Source
AI summary
Generating trial states for a variational quantum Eigenvalue solver (VQE) using a quantum computer is described. An example method includes selecting a number of samples S to capture from qubits for a particular trial state. The method further includes mapping a Hamiltonian to the qubits according the trial state. The method further includes setting up an entangler in the quantum computer, the entangler defining an entangling interaction between a subset of the qubits of the quantum computer. The method further includes reading out qubit states after post-rotations associated with Pauli terms in the target Hamiltonian, the reading out being performed for S samples. The method further includes computing an energy state using the S qubit states. The method further includes, in response to the estimated energy state not converging with an expected energy state, computing a new trial state for the VQE and iterating to compute the estimated energy using the new trial state.