Quantum System Simulation Using DMET and Unentangled QPUs
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Solution Overview
Problem
Existing quantum computing systems face limitations in simulating quantum systems with Hamiltonians of larger dimensions than the number of available qubits, and existing algorithms like phase estimation and variational quantum eigensolvers are constrained by quantum coherence time.
Innovation Solution
A hybrid quantum-classical system utilizing multiple unentangled quantum processor units (QPUs) and density matrix embedding theory (DMET) algorithms to simulate quantum systems, allowing for parallel computations and simulation of larger Hamiltonians.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing quantum algorithms like phase estimation and variational quantum eigensolvers are used, then quantum system simulation is achieved, but the simulation is constrained by quantum coherence time
Solution Approach 1:
The patent divides the quantum system simulation into multiple independent fragments, each processed by separate quantum processor units. This segmentation allows parallel execution of simulations on different fragments simultaneously, reducing the total simulation time and avoiding the quantum coherence time constraint that would limit sequential processing of the entire system.
Solution Approach 2:
The patent combines multiple unentangled quantum processor units to work together on simulating a single quantum system. Each QPU handles a separate fragment, and their results are integrated to provide the complete system simulation, thereby achieving higher reliability and extended effective simulation duration beyond what a single QPU could achieve.
2Quantity of substance
If the number of qubits is limited, then the quantum computer operates within hardware constraints, but it cannot simulate quantum systems with larger Hamiltonian dimensions
Solution Approach 1:
The patent segments the large-dimensional Hamiltonian into multiple smaller fragment Hamiltonians, each suitable for simulation with the available qubits. This allows the system to simulate quantum systems with effectively larger Hamiltonian dimensions by processing smaller manageable pieces in parallel, overcoming the limitation of having fewer qubits than the total system dimension requires.
Solution Approach 2:
The patent makes each quantum processor unit universal by designing it to handle different fragments of the quantum system. The same QPU architecture can be applied to various fragments with different dimensions, allowing the system to scale its simulation capability by adding more QPUs rather than requiring a single large-scale quantum computer.
3Productivity
If multiple quantum processor units are used in parallel, then simulation speed increases, but the system requires coordination between multiple unentangled QPUs
Solution Approach 1:
The patent introduces a classical controller as an intermediary that coordinates between multiple unentangled quantum processor units. The classical controller manages the distribution of fragments to QPUs, collects results from each QPU, and integrates them into the final simulation output, thereby enabling parallel processing without requiring direct quantum entanglement between QPUs.
Solution Approach 2:
The patent segments the simulation task into independent fragment computations that can be executed in parallel on separate QPUs. This segmentation of the computational workload allows each QPU to operate independently at full speed without requiring complex inter-QPU quantum communication, thus achieving high productivity while maintaining relatively simple device architecture.
Data Source
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AI summary
In some aspects, a quantum simulation method includes generating a set of models representing a quantum system. The set of models includes subsystem models representing respective fragments of the quantum system. The quantum system is simulated by operating the set of models on a computer system that includes a classical processor unit and multiple unentangled quantum processor units (QPUs), and the unentangled QPUs operate the respective subsystem models. In some examples, density matrix embedding theory (DMET) is used to compute an approximate ground state energy for the quantum system.