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Avatar Tulsi

Publications and source records attributed to Avatar Tulsi.

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Sandwich test for Quantum Phase Estimation

Quantum Phase Estimation (QPE) has potential for a scientific revolution through numerous practical applications like finding better medicines, batteries, materials, catalysts etc. Many QPE algorithms use the Hadamard test to estimate $\langle \psi|U^{k}|\psi\rangle$ for a large integer $k$ for an efficiently preparable initial state $|\psi\rangle$ and an efficiently implementable unitary operator $U$. The Hadamard test is hard to implement because it requires controlled applications of $U^{k}$. Recently, a Sequential Hadamard test (SHT) was proposed (arXiv:2506.18765) which requires controlled application of $U$ only but its total run time $T_{\rm tot}$ scales as $\mathcal{O}(k^{3}/\epsilon^{2}r_{\rm min}^{2})$ where $r_{\rm min}$ is the minimum value of $|\langle \psi|U^{k'}|\psi\rangle|$ among all integers $k' \leq k$. Typically $r_{\rm min}$ is exponentially low and SHT becomes too slow. We present a new algorithm, the SANDWICH test to address this bottleneck. Our algorithm uses efficient preparation of the initial state $|\psi\rangle$ to efficiently implement the SPROTIS operator $R_{\psi}^{\phi}$ where SPROTIS stands for the Selective Phase Rotation of the Initial State. It sandwiches the SPROTIS operator between $U^{a}$ and $U^{b}$ for integers $\{a,b\} \leq k$ to estimate $\langle \psi|U^{k}|\psi\rangle$. The total run time $T_{\rm tot}$ is $\mathcal{O}(k^{2}\ln k/ \epsilon^{2} s_{\rm min}^{6})$. Here $s_{\rm min}$ is the minimum value of $|\langle \psi|U^{\hat{k}}|\psi\rangle$ among all integers $\hat{k}$ which are values of the nodes of a random binary sum tree whose root node value is $k$ and leaf nodes' values are $1$ or $0$. It can be reasonably expected that $s_{\rm min} \not\ll 1$ in typical cases because there is wide freedom in choosing the random binary sum tree.

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Universal Quantum Algorithm

Quantum amplitude amplification and quantum phase estimation are two fundamental quantum algorithms. All known quantum algorithms are derived from these two algorithms. Even the adiabatic quantum algorithms can also be efficiently simulated using quantum phase estimation. We present a universal quantum algorithm which explains these two algorithms as special cases. An interesting result is that we do not need quantum fourier transform to do quantum phase estimation.

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A simpler algorithm to mark the unknown eigenstates

For an unknown eigenstate $|ψ\rangle$ of a unitary operator $U$, suppose we have an estimate of the corresponding eigenvalue which is separated from all other eigenvalues by a minimum gap of magnitude $Δ$. In the eigenstate-marking problem (EMP), the goal is to implement a selective phase transformation of the $|ψ\rangle$ state (known as \emph{marking} the $|ψ\rangle$ state in the language of the quantum search algorithms). The EMP finds important applications in the construction of several quantum algorithms. The best known algorithm for the EMP combines the ideas of the phase estimation algorithm and the majority-voting. It uses $Θ(\frac{1}Δ\ln \frac{1}ε)$ applications of $U$ where $ε$ is the tolerable error. It needs $Θ\left(\ln \frac{1}Δ\right)$ ancilla qubits for the phase estimation and another $Θ\left(\ln \frac{1}ε\right)$ ancilla qubits for the majority-voting. In this paper, we show that the majority-voting is not a crucial requirement for the EMP and the same purpose can also be achieved using the fixed-point quantum search algorithm which does not need any ancilla qubits. In the case of majority-voting, these ancilla qubits were needed to do controlled transformations which are harder to implement physically. Using fixed-point quantum search, we get rid off these $Θ\left(\ln \frac{1}ε\right)$ ancilla qubits and same number of controlled transformations. Thus we get a much simpler algorithm for marking the unknown eigenstates. However, the required number of applications of $U$ increases by the factor of $Θ\left(\ln \frac{1}ε\right)$. This tradeoff can be beneficial in typical situations where spatial resources are more constrained or where the controlled transformations are very expensive.

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On the class of diffusion operators for fast quantum search

Grover's quantum search algorithm evolves a quantum system from a known source state $|s\rangle$ to an unknown target state $|t\rangle$ using the selective phase inversions, $I_{s}$ and $I_{t}$, of these two states. In one of the generalizations of Grover's algorithm, $I_{s}$ is replaced by a general diffusion operator $D_{s}$ having $|s\rangle$ as an eigenstate and $I_{t}$ is replaced by a general selective phase rotation $I_{t}^ϕ$. A fast quantum search is possible as long as the operator $D_{s}$ and the angle $ϕ$ satisfies certain conditions. These conditions are very restrictive in nature. Specifically, suppose $|\ell\rangle$ denote the eigenstates of $D_{s}$ corresponding to the eigenphases $θ_{\ell}$. Then the sum of the terms $|\langle \ell|t\rangle|^{2}\cot(θ_{\ell}/2)$ over all $\ell \neq s$ has to be almost equal to $\cot(ϕ/2)$ for a fast quantum search. In this paper, we show that this condition can be significantly relaxed by introducing appropriate modifications of the algorithm. This allows access to a more general class of diffusion operators for fast quantum search.

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Success criteria for quantum search on graphs

We consider quantum search on graphs. Recently, it has been shown that the graph properties like connectivity, global symmetry, or regularity cannot serve as a reliable criteria that must be satisfied by a graph to allow a successful quantum search. It is an open question whether it is possible to find such a criteria. We solve this question by giving an affirmative answer.

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Robust quantum spatial search

Quantum spatial search has been widely studied with most of the study focusing on quantum walk algorithms. We show that quantum walk algorithms are extremely sensitive to systematic errors. We present a recursive algorithm which offers significant robustness to certain systematic errors. To search N items, our recursive algorithm can tolerate errors of size O(1/\sqrt{\ln N}) which is exponentially better than quantum walk algorithms for which tolerable error size is only O(\ln N/\sqrt{N}). Also, our algorithm does not need any ancilla qubit. Thus our algorithm is much easier to implement experimentally compared to quantum walk algorithms.

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Phase estimation using an approximate eigenstate

A basic building block of many quantum algorithms is the Phase Estimation algorithm (PEA). It estimates an eigenphase $ϕ$ of a unitary operator $U$ using a copy of the corresponding eigenstate $|ϕ\rangle$. Suppose, in place of $|ϕ\rangle$, we have a copy of an approximate eigenstate $|ψ\rangle$ whose overlap magnitude with $|ϕ\rangle$ is at least $\sqrt{2/3}$. Then PEA fails with a constant probability. However, using multiple copies of $|ψ\rangle$, the failure probaility can be made to decrease exponentially with the number of copies. In this paper, we show that as long as we can perform a selective inversion of $|ψ\rangle$, a single copy is sufficient to estimate $ϕ$. An important application is to improve the spatial complexity of eigenpath traversal algorithm, a "digital" analogue of quantum adiabatic evolution, having applications ranging from quantum physics simulation to optimization. Here the goal is to travel a path of eigenstates of $n$ different unitary operators satisfying some conditions. The fastest algorithm is due to Boixo, Knill and Somma (BKS) which needs $Θ(\ln n)$ copies of the eigenstate. Using our algorithm, BKS algorithm can work using just a single copy of the eigenstate.

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Postprocessing can speed up general quantum search algorithms

A general quantum search algorithm aims to evolve a quantum system from a known source state $|s\rangle$ to an unknown target state $|t\rangle$. It uses a diffusion operator $D_{s}$ having source state as one of its eigenstates and $I_{t}$, where $I_ψ$ denotes the selective phase inversion of $|ψ\rangle$ state. It evolves $|s\rangle$ to a particular state $|w\rangle$, call it w-state, in $O(B/α)$ time steps where $α$ is $|\langle t|s\rangle|$ and $B$ is a characteristic of the diffusion operator. Measuring the w-state gives the target state with the success probability of $O(1/B^{2})$ and $O(B^{2})$ applications of the algorithm can boost it from $O(1/B^{2})$ to $O(1)$, making the total time complexity $O(B^{3}/α)$. In the special case of Grover's algorithm, $D_{s}$ is $I_{s}$ and $B$ is very close to $1$. A more efficient way to boost the success probability is quantum amplitude amplification provided we can efficiently implement $I_{w}$. Such an efficient implementation is not known so far. In this paper, we present an efficient algorithm to approximate selective phase inversions of the unknown eigenstates of an operator using phase estimation algorithm. This algorithm is used to efficiently approximate $I_{w}$ which reduces the time complexity of general algorithm to $O(B/α)$. Though $O(B/α)$ algorithms are known to exist, our algorithm offers physical implementation advantages.

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Quantum search algorithm tailored to clause satisfaction problems

Many important computer science problems can be reduced to clause satisfaction problem. We are given $n$ Boolean variables $x_{k}$ and $m$ clauses $c_{j}$ where each clause is a function of values of some of the variables. We want to find an assignment $i$ of variables for which all $m$ clauses are satisfied. Let $f_{j}(i)$ be a binary function which is $1$ if $j^{\rm th}$ clause is satisfied by the assignment $i$ else $f_{j}(i) = 0$. Then the solution is $r$ for which $f(i=r) = 1$, where $f(i)$ is the AND function of all $f_{j}(i)$. In quantum computing, Grover`s algorithm can be used to find $r$. A crucial component of this algorithm is the selective phase inversion $I_{r}$ of the solution state encoding $r$. $I_{r}$ is implemented by computing $f(i)$ for all $i$ in superposition which requires computing AND of all $m$ binary functions $f_{j}(i)$. Hence there must be coupling between the computation circuits for each $f_{j}(i)$. In this paper, we present an alternative quantum search algorithm which relaxes the requirement of such couplings. Hence it offers implementation advantages for clause satisfaction problems.

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Faster quantum searching with almost arbitrary operators

Grover search algorithm drives a quantum system from an initial state to a desired final state by using selective phase inversions of these two states. In (1), we studied a generalization of Grover algorithm which relaxes the assumption of the efficient implementation of the selective phase inversion of the initial state, also known as diffusion operator. This assumption is known to become a serious handicap in cases of physical interest (2,3,4,5). Our general search algorithm works with almost arbitrary diffusion operator with only restriction of having the initial state as one of its eigenstates. The price that we pay for using arbitrary operator is an increase in the number of oracle queries by a factor of order of B, where B is a characteristic of the eigenspectrum of diffusion operator and it can be large in some situations. Here we show that by using quantum fourier transform, we can regain the optimal query complexity of Grover algorithm without losing the freedom of using arbitrary diffusion operators for quantum searching. However, the total number of operators required by algorithm is still order of B times more than that of Grover algorithm. So our algorithm offers advantage only if oracle operator is computationally more expensive than diffusion operator, which is true in most search problems.

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Optimal quantum searching to find a common element of two sets

Given two sets A and B and two oracles O(A) and O(B) that can identify the elements of these sets respectively, the goal is to find an element common to both sets using minimum number of oracle queries. Each application of either O(A) or O(B) is taken as a single oracle query. This is basically a search problem and a straightforward application of Grover algorithm can solve this problem but its performance is slow compared to the optimal one by a constant factor of 1.57. Here we present a variant of Grover algorithm which achieves the optimal performance in not too restrictive cases.

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Experimental NMR implementation of a robust quantum search algorithm

Grover's quantum search algorithm, involving a large number of qubits, is highly sensitive to errors in the physical implementation of the unitary operators. This poses an intrinsic limitation to the size of the database that can be practically searched. The lack of robustness of Grover's algorithm for a large number of qubits is due to quite stringent "phase-matching" condition. To overcome this limitation, Tulsi suggested a modified search algorithm [PRA 78, 022332] which succeeds as long as the errors are reproducible and reversible while Grover's algorithm fails. Such systematic errors arise often from imperfections in apparatus setup e.g. the errors arising from imperfect pulse calibration and offset effect in NMR systems. In this paper, we report the experimental NMR implementation of the modified search algorithm and its comparison with the original Grover's algorithm. We experimentally validate the theoretical predictions made by Tulsi.

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Adiabatic Quantum Computation with a 1D projector Hamiltonian

Adiabatic quantum computation is based on the adiabatic evolution of quantum systems. We analyse a particular class of qauntum adiabatic evolutions where either the initial or final Hamiltonian is a one-dimensional projector Hamiltonian on the corresponding ground state. The minimum energy gap which governs the time required for a successful evolution is shown to be proportional to the overlap of the ground states of the initial and final Hamiltonians. We show that such evolutions exhibit a rapid crossover as the ground state changes abruptly near the transition point where the energy gap is minimum. Furthermore, a faster evolution can be obtained by performing a partial adiabatic evolution within a narrow interval around the transition point. These results generalize and quantify earlier works.

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General framework for quantum search algorithms

Grover's quantum search algorithm drives a quantum computer from a prepared initial state to a desired final state by using selective transformations of these states. Here, we analyze a framework when one of the selective trasformations is replaced by a more general unitary transformation. Our framework encapsulates several previous generalizations of the Grover's algorithm. We show that the general quantum search algorithm can be improved by controlling the transformations through an ancilla qubit. As a special case of this improvement, we get a faster quantum algorithm for the two-dimensional spatial search.

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Quantum computers can search rapidly by using almost any selective transformations

The search problem is to find a state satisfying certain properties out of a given set. Grover's algorithm drives a quantum computer from a prepared initial state to the target state and solves the problem quadratically faster than a classical computer. The algorithm uses selective transformations to distinguish the initial state and target state from other states. It does not succeed unless the selective transformations are very close to phase-inversions. Here we show a way to go beyond this limitation. An important application lies in quantum error-correction, where the errors can cause the selective transformations to deviate from phase-inversions. The algorithms presented here are robust to errors as long as the errors are reproducible and reversible. This particular class of systematic errors arise often from imperfections in apparatus setup. Hence our algorithms offer a significant flexibility in the physical implementation of quantum search.

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Faster quantum walk algorithm for the two dimensional spatial search

We consider the problem of finding a desired item out of $N$ items arranged on the sites of a two-dimensional lattice of size $\sqrt{N} \times \sqrt{N}$. The previous quantum walk based algorithms take $O(\sqrt{N}\log N)$ steps to solve this problem, and it is an open question whether the performance can be improved. We present a new algorithm which solves the problem in $O(\sqrt{N\log N})$ steps, thus giving an $O(\sqrt{\log N})$ improvement over the known algorithms. The improvement is achieved by controlling the quantum walk on the lattice using an ancilla qubit.

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