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Part (a) shows that $\mathcal{L}$ is invariant throughout the interpolation. As the Hamiltonians are Hermitian, ${\mathcal{L}}^{\perp }$ is invariant as well: evolution starting in $\mathcal{L}$ has no leakage into other sectors. The quantum adiabatic theorem may therefore be applied using the restricted spectral gap, even if other sectors of the full Hamiltonian have different low-energy levels.

Combining (b)(iv) with the supplied bound for $s>1/3$ gives ${\min }_s\Delta (M(s))\ge c/(T+1{)}^2$ for a constant $c>0$. The matrix $E-D$ has diagonal entries $1/2,0,\dots ,0,-1/2$ and off-diagonal entries $-1/2$. Its Gershgorin discs all lie in $[-1,1]$, so $\Vert E-D\Vert \le 1$. Thus the runtime criterion for adiabatic preparation of a computational history state is satisfied bywith a sufficiently large constant.

Starting from the supplied initial ground state $|e_0⟩$, the resulting state ${\rho }_{\mathrm{out}}$ obeysHere the trace norm follows the question's convention; the conventional trace distance is half this norm. The Hamiltonian has $O(T)$ explicitly specified terms, each acting on at most five qubits, so its description and the runtime are polynomial in circuit size. If ${ϵ}^{-1}$ is polynomially bounded, the whole preparation has polynomial cost. The initial computational input is assumed to have been prepared, as in the premise of the quantum adiabatic theorem.

A promise problem $(L_{\mathrm{yes}},L_{\mathrm{no}})$ belongs to QMA if there are polynomials $p,q$ and a uniform family of quantum circuits $V_x$ of size at most $q(|x|)$, taking a $p(|x|)$-qubit witness and clean ancillas, with one output qubit interpreted as acceptance, such thatNo condition is imposed outside the promise. The witness may be an arbitrary density operator, and the verifier is efficient but need not know how to prepare an honest witness. Pure witnesses suffice for completeness because the acceptance probability is linear in the witness. QMA is quantum verification with polynomially many witness qubits and a constant completeness-soundness gap.

The local Hamiltonian problem is a promise problem specified by a sum $H={\sum }_{j=1}^M{h}_j$ on $n$ qubits, where $M$ is polynomial in $n$, each Hermitian term acts on at most a fixed number $k$ of qubits, and the terms and two thresholds $a<b$ are described with polynomially many bits. The promise gap satisfies $b-a\ge 1/\mathrm{poly}(n)$. In a convenient normalization, $0\le h_j\le I$. The task is to distinguishThere is no required answer when the minimum energy lies between the thresholds. A formulation with Hermitian terms of polynomially bounded norm is equivalent: shift each term by a known scalar lower bound and rescale all terms by a polynomial bound to obtain positive semidefinite terms of norm at most one, while shifting and rescaling $a,b$ in parallel. The promise gap remains inverse-polynomial.

Use an $n$-qubit state as the witness. Uniformly choose a term $h_j$ and perform the two-outcome positive operator-valued measure $\{I-h_j,h_j\}$. Regard the second outcome as an energy flag. For witness $\rho$, its probability isThis is an efficient local-energy measurement verification: a unitary operator on the $k$ relevant qubits and one ancilla qubit can implement the measurement using the isometryThe local dimension is constant; the term matrices and the measurement can be approximated to inverse-polynomial accuracy. Uniform choice of $j$ and the classical threshold comparison also have polynomial overhead.

SetIn the nontrivial case $0\le a<b\le M$, $\delta$ is inverse-polynomial. Cases outside this energy range are decided directly from $0\le H\le MI$.

Ask for $r$ witness registers, measure an independently sampled local term on each, and accept if the mean number of energy flags is at most $\theta$. An honest product ground state on a YES instance has flag probability at most $p_{\mathrm{y}}$. The Hoeffding inequality gives failure at most $e^{-r{\delta }^2/2}$. For completeness, the required Bernoulli concentration estimate follows by bounding the centered log moment-generating function $f(\lambda )=\ln \mathbb{E}{e}^{\lambda (Y-p)}$: $f(0)=f^{\prime }(0)=0$ and $f^{\prime \prime }(\lambda )$ is a tilted Bernoulli variance, at most $1/4$. Hence $f(\lambda )\le {\lambda }^2/8$; exponential Markov's inequality optimized at $\lambda =4u$ yields $\mathrm{Pr}(\overline{Y}-p\ge u)\le e^{-2r{u}^2}$, and the lower-tail version follows in the same way.

Soundness must also cover entangled witness registers. The averaged flag effect is $R=H/M$. All its eigenvalues are at least $p_{\mathrm{n}}$ on a NO instance. The repeated verifier's acceptance effect is a polynomial in the commuting tensor-factor operators $R_j$ and $I-R_j$. In their product eigenbasis its eigenvalues are lower-tail probabilities of independent Bernoulli variables whose parameters are each at least $p_{\mathrm{n}}$. Their lower tail is bounded by the identical-parameter tail at $p_{\mathrm{n}}$, using the same uniform-variable coupling as in QMA parallel repetition with entangled witnesses. Therefore its operator norm is at most $e^{-r{\delta }^2/2}$, which bounds acceptance for any entangled witness.

Choosinggives completeness at least $2/3$ and soundness at most $1/3$. A slightly larger constant absorbs implementation errors; for example, approximate each flag effect in norm to at most $\delta /8$, retain a gap at least $3\delta /4$, and increase $r$ by a constant factor. The total witness length $rn$ and circuit size are polynomial. Thus

The filtered decomposition is not necessarily frustration-free. Commuting with $P_0$ makes $|{\varphi }_0⟩$ an eigenvector of each term; it does not make that eigenvector a lowest-energy state of each term. This is the distinction expressed by commuting with a ground projector does not imply frustration freeness.

For an explicit positive, two-local counterexample on three qubits, let $P_0^{(i)}=|0⟩⟨0{|}_i$ and $P_1^{(i)}=|1⟩⟨1{|}_i$, and use the two distinct interaction sets $\{1,2\}$ and $\{2,3\}$:Both are positive semidefinite and commute with their sum. The total energy on a computational basis vector isThus $H=h_{12}+h_{23}$ has unique ground state $|000⟩$, energy $1$, and spectral gap $1$. Since $[H,h_{12}]=[H,h_{23}]=0$, spectral filtering of Hamiltonian terms leaves both terms unchanged for every normalized filter:But $h_{12}|000⟩=|000⟩$, whereas $h_{12}|010⟩=0$. The global ground state therefore does not minimize $g^{(12)}$, proving that the resulting decomposition is not a frustration-free Hamiltonian.

There is a useful positive result if the original decomposition already is a frustration-free Hamiltonian. If $a_Z={\lambda }_{\min }(h_Z)$ and $h_Z|{\varphi }_0⟩=a_Z|{\varphi }_0⟩$, then $h_Z-a_{Z}I\ge 0$. Averaging its unitary conjugates with nonnegative $w$ gives $g^{(Z)}-a_{Z}I\ge 0$, while $g^{(Z)}|{\varphi }_0⟩=a_Z|{\varphi }_0⟩$. Thus filtering preserves existing frustration freeness; it does not create it for an arbitrary gapped Hamiltonian.

Let $L=\lceil {\log }_{2}N\rceil$. In the Shor algorithm, first use classical arithmetic to deal with even $N$ and perfect powers, and to reject prime inputs. Perfect-power recognition and primality testing have polynomial-time classical algorithms. Factoring a perfect power $b^k$, with $b,k>1$, already gives a nontrivial factor $b$. We may therefore consider an odd composite $N$ with at least two distinct prime factors.

Choose $\alpha$ uniformly from $1<\alpha <N$ and compute its greatest common divisor with $N$ using the Euclidean algorithm. A proper nontrivial divisor ends the computation. Otherwise $\alpha$ is a unit, and the remaining task is quantum order finding. SetPrepare a uniform quantum superposition in a $t$-qubit register and use quantum modular exponentiation in a second register to obtainRepeated squaring and reversible modular multiplication implement this step with polynomially many quantum gates in $L$. Apply the inverse quantum Fourier transform to the first register and perform a quantum measurement in the computational basis, obtaining $j$. Measuring the second register first, and Fourier-transforming the resulting periodic coset, gives the same distribution of $j$.

Here is a quantitative quantum Fourier sampling bound for order finding. If $r={\mathrm{ord}}_N(\alpha )$, multiplication by $\alpha$ cycles its $r$ orbit states. The Fourier eigenstates of that cycle have eigenvalues $e^{2\pi is/r}$, and the initial state $|1⟩$ has squared overlap $1/r$ with each, for $0\le s<r$. ThereforeConditioned on a particular eigenphase $s/r$, its nearest integer outcome $j_s$ obeys $|j_s/Q-s/r|\le 1/(2Q)$ and has probability at least $4/{\pi }^2$. Indeed the absolute amplitude is $|\sin (\pi Q\delta )/(Q\sin (\pi \delta ))|$, where $\delta =s/r-j_s/Q$. For $|Q\delta |\le 1/2$, the numerator is at least $2Q|\delta |$ and the denominator at most $\pi Q|\delta |$; the limiting value at $\delta =0$ is one.

If $\gcd (s,r)=1$, the fraction $s/r$ is reduced, and $r<N$ givesUse continued-fraction recovery in quantum order finding. The relevant approximation theorem says that reduced fractions satisfying $|a/b-p/q|<1/(2q^2)$ are continued fraction convergents of $a/b$. For an input rational of $O(L)$ digits, their $O(L)$ candidates can be enumerated classically in $O(L^3)$ time, and their denominators are at most the reduced input denominator. This is the supplied continued-fraction result; discard candidate denominators exceeding $N$. A zero measurement is treated as a failed order-finding sample.

For each remaining even candidate denominator $q$, compute $z={\alpha }^{q/2}\mathrm{mod}N$ by repeated squaring, and try $\gcd (z-1,N)$ and $\gcd (z+1,N)$. Return only a divisor $d$ with $1<d<N$; otherwise repeat the quantum sample or choose another $\alpha$. This candidate-denominator gcd post-processing cannot return an incorrect factor, since its output is checked directly. When the recovered denominator is the true even multiplicative order and the square root of one is nontrivial, part (i) guarantees success. Checking ${\alpha }^q\equiv 1(\mathrm{mod}N)$ before trying the gcds is also a valid, more restrictive convention; the single-run distinction matters in part (iii).

Two further standard quantitative facts explain why repetitions remain efficient. First, for a uniformly chosen unit modulo an odd integer with at least two distinct prime divisors, the probability of a useful unit in Shor factorization is at least $1/2$: its true multiplicative order is even and ${\alpha }^{r/2}\not\equiv -1(\mathrm{mod}N)$. To see the source of this bound, the Chinese remainder theorem for unit groups makes the prime-power components independent. Each odd-prime-power unit group is cyclic. If its 2-primary order is $2^v$, the probability of the largest possible value of $v$ is $1/2$, and the probability of any specified smaller value is at most $1/2$. Failure requires all components to have the same $v$: either all zero, making $r$ odd, or all the same positive value, making the halfway power $-1$ in every component. Conditioning on one component, the chance another component matches it is at most $1/2$, so total failure is at most $1/2$.

Second, the fraction of phase numerators coprime to $r$ is $\phi (r)/r$, where $\phi$ is the Euler totient function. The elementary totient-ratio lower bound suffices: if the distinct prime divisors are $p_1<\cdots <p_k$, then $p_j\ge j+1$ and $k\le {\log }_{2}r$. The totient product formula therefore givesThus for a good $\alpha$, the probability of sampling a coprime phase numerator with the required accuracy is at least $(4/{\pi }^2)\phi (r)/r$. This is inverse-polynomial in the input length, so polynomially many repetitions achieve any fixed success probability. The quantum Fourier transform uses $O(t^2)$ elementary rotations and Hadamard gates in the ideal gate description, and the other quantum and classical stages are polynomial in $L$.

The reduction is therefore polynomial in the bit length:A small denominator need not itself be the true multiplicative order; verification of the final factor is essential, and no guarantee is claimed for every individual sample.

Prepare an ancillary qubit inIt can be prepared from $|0⟩$ by an $X$ operation followed by a Hadamard gate, independently of $g$. One call to the Boolean quantum oracle then gives quantum phase kickback:The data state receives a minus sign precisely on the basis vectors spanning $\mathcal{G}$, while the ancilla remains unchanged and unentangled. One oracle call realizes the reflection. By linearity it is the marked-state phase oraclewith one oracle call. The ancillary preparation and its optional inverse use only fixed operations, so no additional knowledge of $g$ is needed.

Write $|a⟩={⨂}_j|a_j⟩$. Instead of storing the output vector, use Heisenberg propagation of a Pauli observable through a Clifford circuit:Initialize the compact Pauli group representation at $Z_1$ and conjugate successively by $U_N,U_{N-1},\dots ,U_1$, in that order, using $P\mapsto U_j^{†}P{U}_j$. This giveswith $O(N+n)$ local-update work and $O(n)$ storage. The resulting Pauli operator is Hermitian, so we can express it as $P=\eta {⨂}_j{\sigma }_j$, where $\eta \in \{+1,-1\}$ and each ${\sigma }_j$ is $I,X,Y$ or $Z$. Any factors $XZ$ in the representation from part (i) are converted using $XZ=-iY$, with their phases absorbed into $\eta$.

The product state input now makes the expectation factorize:Each factor is a two-by-two matrix calculation. More explicitly, for $|a_j⟩={\alpha }_j|0⟩+{\beta }_j|1⟩$, the three nontrivial expectations are $2\mathrm{Re}({\overline{\alpha }}_j{\beta }_j)$, $2\mathrm{Im}({\overline{\alpha }}_j{\beta }_j)$, and $|{\alpha }_j{|}^2-|{\beta }_j{|}^2$ for $X,Y,Z$, respectively.

Thus this single-output process has a strong classical simulation in polynomial time, even when the individual input states are not stabilizer states. As usual, the given state identities must provide efficiently computable amplitudes. With finite-precision inputs, each factor can be evaluated to error $\delta /n$ and its approximation clipped to $[-1,1]$ to obtain the final expectation to error at most $\delta$, because all factors lie in $[-1,1]$. The required extra precision is only $O(\log (n/\delta ))$ bits. Exact evaluation applies when the input descriptions support exact arithmetic. This avoids silently assigning a finite exact-computation cost to arbitrary unspecified real numbers.

Once the probabilities are known, a classical randomized computation can reproduce this measured bit. The argument concerns the specified one-qubit output; it does not assert that every joint measurement distribution for arbitrary product inputs can be simulated by this same single-observable calculation.

The computational-basis projectors on the first qubit areThe Pauli Z gate acts as $+1$ on the first subspace and $-1$ on the second, so $Z_1={\Pi }_0-{\Pi }_1$. By the Born rule, $p_b=⟨\psi |{\Pi }_b|\psi ⟩$. Therefore the expectation determines the output probabilities:The normalization supplies $p_0+p_1=1$; no product-state assumption is needed for this identity.

Store a tensor-product element of the Pauli group asThe phases of the original single-qubit factors can all be accumulated into $i^s$. This binary phase representation of a Pauli string uses $2n+2$ bits. It retains phases, which must not be dropped when computing probabilities later.

For the conjugation convention $U^{†}PU$ in the question, direct multiplication of the two-by-two matrices givesandThese backward Pauli updates for Hadamard and phase gates use $S^{†}PS$, not $SP{S}^{†}$. On the affected line $j$, the updates areAll phases are reduced modulo four and the updates use the old bits.

For a Controlled-Z gate on lines $j,k$, its diagonal action givesFor the controlled-Z update in the binary phase representation, conjugation respects products, so these determine the rule for every Pauli operator on those lines. In the chosen ordered $XZ$ convention it isThe phase arises when a newly introduced $Z_k$ is moved past $X_k$. For instance $X_j{X}_k$ becomes $-X_j{Z}_j{X}_k{Z}_k$, so this sign matters.

The untouched factors are unchanged. Each Clifford operation therefore needs only a fixed number of local bit updates; reconstructing the requested full list takes $O(n)$ time. Put the final phase into the first factor, which is permitted because the single-qubit Pauli group includes all multiples by $\pm 1,\pm i$. The classical cost is polynomial, despite the exponentially large matrix of the operation on the full state space.

Use GHZ preparation with Hadamard and controlled-Z gates for $n\ge 2$. Apply a Hadamard gate to the first qubit, then a controlled-NOT gate from that qubit to each of the other $n-1$ qubits. Every controlled-NOT can be written in the allowed gate set asIndeed conjugating the target $Z$ by $H$ makes the controlled phase into a controlled $X$. The rightmost gate acts first. Consequently the Clifford circuit uses $1+3(n-1)$ allowed gates and givesFor every chosen qubit $j$, tracing out the other qubits yields ${\rho }_j=\frac{1}{2}(|0⟩⟨0|+|1⟩⟨1|)=I/2$. Equivalently, across that qubit versus the rest, the displayed state is a Schmidt decomposition with two nonzero coefficients $1/\sqrt{2}$. It is therefore entangled across every such bipartition.

Entanglement does not prevent the polynomial single-output simulation from part (iii). The simulation follows the measured observable rather than claiming that the intermediate states remain product states. When $n=1$, there is no remaining subsystem and the requested entanglement is impossible; the construction and claim require $n\ge 2$.

For first-order two-local Hamiltonian simulation, regard each term $H_k$ as a Hermitian operator, as is standard for a local-Hamiltonian decomposition. The Lie-Trotter product formula for bounded operators statesin operator norm; the reversed factor ordering is just as valid as the displayed one. Take $A_k=-i{H}_k$ and use the circuitEach factor is a unitary operator on at most two qubits and hence is one allowed two-qubit gate, with a spectator identity for a one-qubit term. The factors in the rightmost part of each written product act first. Merely quoting the limit would not establish the requested rate, so we give a quantitative error bound.

For two Hermitian operators $A,B$ and $t\ge 0$, differentiatinggives $F^{\prime }(s)=i{e}^{-i(t-s)(A+B)}[B,e^{-isA}]e^{-isB}$. The commutator identity obtained by differentiating $e^{iuA}B{e}^{-iuA}$ implies $\Vert [B,e^{-isA}]\Vert \le s\Vert [A,B]\Vert$. Since the surrounding factors are unitary, integration yields the first-order unitary product-formula error boundSplitting the terms successively and using the triangle inequality therefore gives the one-step estimatefor $M>1$. Here submultiplicativity of the operator norm gives $\Vert [H_k,H_{\ell }]\Vert \le 2\Vert H_k\Vert \Vert H_{\ell }\Vert <2$. The ordering chosen for the products changes the sign of local commutators, not this norm bound. Applying part (i) to the $r$ steps givesChoose $r=\max (1,\lceil M(M-1)/(2ϵ)\rceil )$. For $M>1$ the error is strictly below $ϵ$; $M=1$ is exact already with $r=1$. The quantum circuit contains $Mr$ two-qubit gates. A sufficient gate count isThus the worst-case polynomial degree in this first-order bound is six. Terms with disjoint supports commute and may improve the count in particular decompositions, but the stated assumptions alone suffice for this bound, without a further sparsity or bounded-degree condition. The requested gate model allows arbitrary two-qubit unitaries, so no additional compilation error is incurred here.