Quantum Eigenstate Locking With Adaptive Phase Shift Feedback
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Solution Overview
Problem
Existing methods struggle to efficiently prepare target quantum states, particularly in many-body quantum systems, due to difficulties in finding suitable and efficient methods to stabilize the system in a target eigenstate, especially when the initial state has limited overlap with the target state.
Innovation Solution
An adaptive phase shift method is employed, iteratively updating phase gates using current average energy estimates of the quantum system, combined with ancilla qubits to steer the quantum system towards the target eigenstate, utilizing classical processors and quantum computing devices to enhance the probability of locking into the target state and reduce noise impact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing methods are used to prepare target quantum states, then the process is simple, but the efficiency and ability to stabilize the system in a target eigenstate is poor
Solution Approach 1:
The patent implements an adaptive phase shift method where the phase shift parameter is dynamically updated based on feedback from the quantum system's average energy estimate. At each iteration, the classical processor receives energy measurement data, updates the phase shift parameter accordingly, and applies it to the quantum system. This closed-loop feedback mechanism enables efficient convergence to the target eigenstate by continuously adjusting the phase shift to maximize the overlap with the target state, thereby resolving the contradiction between preparation efficiency and method complexity.
2Adaptability or versatility
If the initial state has limited overlap with the target state, then the method should work for any initial state, but the convergence speed decreases
Solution Approach 1:
The patent employs a dynamic phase shift parameter that evolves iteratively based on the quantum system's state. Rather than using a fixed phase shift, the method continuously adapts the phase shift parameter at each iteration based on the current average energy estimate and overlap with the target state. This dynamic adjustment allows the system to efficiently navigate from any initial state with non-zero overlap to the target eigenstate, resolving the contradiction between universal applicability and convergence speed by making the phase shift adaptive rather than static.
3Reliability
If noise is present in the quantum system, then the system is more robust, but noise-induced transitions to non-target states increase
Solution Approach 1:
The adaptive phase shift method incorporates feedback from energy measurements to dynamically adjust the phase shift parameter, which helps counteract noise-induced transitions. By continuously monitoring the quantum system's energy and updating the phase shift accordingly, the method can correct deviations from the target eigenstate caused by noise. This feedback mechanism enhances robustness against noise while minimizing harmful transitions, as the adaptive adjustment compensates for noise effects in real-time during the preparation process.
Data Source
AI summary
Methods, systems and apparatus for targeting many-body states on a quantum computer. In one aspect, a method includes an adaptive phase shift method that includes preparing the quantum system in an initial state, wherein the initial state has non-zero overlap with the target eigenstate; preparing an ancilla qubit in a zero computational basis state; and iteratively applying a quantum eigenstate locking circuit to the quantum system and ancilla qubit until the state of the quantum system approximates the target eigenstate, wherein the quantum eigenstate locking circuit comprises a phase gate that, at each n-th iteration, is updated using a current average energy estimate of the quantum system.