Adaptive Phase-Shift Eigenstate Locking on Quantum Computers
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Preparing target states of quantum systems is challenging due to difficulties in efficiently locking into the target state, especially when the initial state has limited overlap, and existing methods are prone to noise and high quantum circuit depth, making them unsuitable for near-term quantum computing devices.
Innovation Solution
The adaptive phase shift technique iteratively updates phase gate parameters to increase the probability of locking into a target state, reduces noise impact, and employs low-depth quantum circuits with ancilla qubits to stabilize the system, allowing for efficient preparation even when the initial state is difficult to prepare, and can be implemented on near-term devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing phase estimation methods are used, then quantum phase learning can be achieved, but the quantum circuit depth becomes high and noise impact increases
Solution Approach 1:
The quantum system is divided into two separate registers: a system register containing the qubits to be prepared, and an ancilla register containing measurement qubits. This segmentation allows the phase estimation to be performed on the ancilla qubits while the system qubits remain in their target state, reducing the overall circuit depth and noise exposure.
Solution Approach 2:
Ancilla qubits are introduced as intermediary elements that interact with the system qubits through controlled operations. The ancilla qubits serve as mediators to extract phase information without requiring deep circuits on the system qubits themselves, thereby reducing noise impact while maintaining measurement precision.
2Productivity
If standard eigenstate preparation methods are used, then target states can be prepared, but the rate of locking into target state is low
Solution Approach 1:
The patent implements an iterative feedback mechanism where measurement results from the ancilla qubits are used to update the phase shift parameters for subsequent iterations. This feedback loop enables the system to progressively lock onto the target eigenstate by adjusting the phase shifts based on observed measurement outcomes, significantly increasing both the preparation rate and locking probability.
Solution Approach 2:
The phase shift parameters are made dynamic rather than static, allowing them to be adjusted iteratively based on measurement results. This dynamic adaptation enables the system to converge faster to the target state by continuously optimizing the phase shifts to maximize the probability of finding the desired eigenstate.
3Measurement precision
If high-depth quantum circuits are used for state preparation, then accurate phase estimation is possible, but noise-induced transitions to undesired eigenstates increase
Solution Approach 1:
By segmenting the quantum system into system qubits and ancilla qubits, the patent isolates the noise-sensitive system qubits from the deep circuit operations. The ancilla qubits bear the burden of the measurement operations, protecting the system qubits from noise-induced transitions while maintaining phase estimation accuracy.
Solution Approach 2:
The ancilla qubits act as intermediaries that absorb the noise impact of measurement operations. By performing all phase estimation operations on the ancilla qubits through controlled interactions, the system qubits are shielded from direct noise exposure, reducing transitions to undesired eigenstates while preserving measurement precision.
4Productivity
If initial states with sufficient overlap with target state are used, then preparation is efficient, but such initial states are difficult to prepare in many cases
Solution Approach 1:
The iterative feedback mechanism compensates for poor initial state overlap by continuously adjusting phase shifts based on measurement results. Even when the initial state has minimal overlap with the target eigenstate, the feedback-driven phase optimization progressively amplifies the target state component, enabling efficient preparation without requiring specially crafted initial states.
Solution Approach 2:
The patent changes the parameters of the quantum system dynamically by adjusting phase shift values across iterations. This parameter optimization allows the system to converge to the target eigenstate regardless of the initial state's overlap, effectively transforming a difficult preparation problem into a solvable optimization process through parameter tuning.
Data Source
Figure 1
Figure 2
Figure 3
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.