Effective 24-Qubit Calculation

• The paper demonstrates that semi-boolean polynomial encoding enables ancilla-free, high-performance arithmetic circuits for modular, signed, and floating-point operations.
• It employs QFT-based methods with phase-cascade modifications to achieve efficient in-place multiplication, optimizing resource usage and circuit depth.
• Optimization techniques, including GMS gate substitutions, significantly reduce two-qubit gate depth while balancing trade-offs with limited ancilla availability.

Effective 24 qubit calculations employ semi-boolean polynomial (SBP) encoding to realize compact, high-performance, ancilla-free arithmetic circuits for unsigned integers, signed integers, and floating-point representations. These constructions optimize resource usage and circuit depth, enabling practical modular arithmetic and constant multiplication in the 24-qubit regime, as presented in the SBP formalism of "Efficient Floating Point Arithmetic for Quantum Computers" (Seidel et al., 2021). The following exposition details the encoding principles, constant multiplication workflow, signed and floating-point extensions, resource breakdown for the 24-qubit case, and trade-offs in circuit realization.

1. Semi-Boolean Polynomial Encoding for Modular Arithmetic

Arithmetic over the ring $\mathbb{Z}/2^m\mathbb{Z}$ can be reformulated as the evaluation of semi-boolean polynomials, in which any $m$-qubit state $\lvert y \rangle$ is prepared in the Fourier basis by $$H^{\otimes m}\lvert 0 \rangle = (1/\sqrt{2^m})\sum_{k=0}^{2^m-1}\lvert k\rangle$$. The SBP construction makes use of the diagonal $G$-gate:

$$U_G(y) = \bigotimes_{i=0}^{m-1} P_i(2\pi y 2^i / 2^m),$$

which imparts a phase $e^{2\pi i y k/2^m}$ on computational basis state $\lvert k \rangle$. Applying an inverse quantum Fourier transform (QFT$^\dagger$) yields $\lvert y \bmod 2^m \rangle$.

A semi-boolean monomial for input vector $m$0 has the form $m$1, for $m$2. The entire polynomial $m$3, comprising a sum over such monomials, is encoded by sequentially applying $m$4 gates controlled by the necessary $m$5 combinations, which are then fused through the additive property:

$m$6

Thus, $m$7 after the SBP circuit.

2. Ancilla-Free In-Place Multiplication by Classical Constants

To multiply an $m$8-qubit register $m$9 by an odd classical constant $\lvert y \rangle$0 (mod $\lvert y \rangle$1), one uses the property that such $\lvert y \rangle$2 are invertible modulo $\lvert y \rangle$3, making the mapping reversible:

$\lvert y \rangle$4

The procedure is QFT-based:

1. Apply QFT on all $\lvert y \rangle$5 qubits.
2. Replace every controlled-phase (CP) gate's angle $\lvert y \rangle$6 in the QFT with $\lvert y \rangle$7 (the "phase-cascade").
3. Apply QFT$\lvert y \rangle$8.

The explicit QFT decomposition is:

$\lvert y \rangle$9

modified for multiplication by $$H^{\otimes m}\lvert 0 \rangle = (1/\sqrt{2^m})\sum_{k=0}^{2^m-1}\lvert k\rangle$$0 as $$H^{\otimes m}\lvert 0 \rangle = (1/\sqrt{2^m})\sum_{k=0}^{2^m-1}\lvert k\rangle$$1, with the Hadamard gates unaffected due to $$H^{\otimes m}\lvert 0 \rangle = (1/\sqrt{2^m})\sum_{k=0}^{2^m-1}\lvert k\rangle$$2. The ancilla-free method requires only the $$H^{\otimes m}\lvert 0 \rangle = (1/\sqrt{2^m})\sum_{k=0}^{2^m-1}\lvert k\rangle$$3 main qubits.

Resource analysis:

• Qubits: $$H^{\otimes m}\lvert 0 \rangle = (1/\sqrt{2^m})\sum_{k=0}^{2^m-1}\lvert k\rangle$$4
• H-gates: $$H^{\otimes m}\lvert 0 \rangle = (1/\sqrt{2^m})\sum_{k=0}^{2^m-1}\lvert k\rangle$$5
• CP-gates: $$H^{\otimes m}\lvert 0 \rangle = (1/\sqrt{2^m})\sum_{k=0}^{2^m-1}\lvert k\rangle$$6
• Depth: $$H^{\otimes m}\lvert 0 \rangle = (1/\sqrt{2^m})\sum_{k=0}^{2^m-1}\lvert k\rangle$$7

3. Extensions: Signed-Integer and Floating-Point Representations

a) Signed-Integer Encoding

Signed $$H^{\otimes m}\lvert 0 \rangle = (1/\sqrt{2^m})\sum_{k=0}^{2^m-1}\lvert k\rangle$$8-bit integers $$H^{\otimes m}\lvert 0 \rangle = (1/\sqrt{2^m})\sum_{k=0}^{2^m-1}\lvert k\rangle$$9 are encoded via the two's-complement ring isomorphism as $G$0. Thus, modular operations (addition, subtraction, multiplication) mod $G$1 directly implement signed arithmetic. The SBP circuit extends to $G$2 qubits for full coverage of the signed range.

b) IEEE-Style Mono-Quantum Floating-Point

Floating-point numbers of the form $G$3 are decomposed as follows:

• Sign: $G$4, encoded in a single qubit.
• Exponent: $G$5, kept classical.
• Mantissa: $G$6, in $G$7 signed qubits.

Arithmetic on floating-point numbers reduces to SBP-encoded operations on the mantissas, with exponent tracking managed classically. e.g., to add $G$8 and $G$9, select an output exponent $$U_G(y) = \bigotimes_{i=0}^{m-1} P_i(2\pi y 2^i / 2^m),$$0, then:

$$U_G(y) = \bigotimes_{i=0}^{m-1} P_i(2\pi y 2^i / 2^m),$$1

4. Explicit 24-Qubit Circuit Example: Ancilla-Free In-Place Multiplication

A concrete instantiation uses all 24 qubits to store an unsigned integer $$U_G(y) = \bigotimes_{i=0}^{m-1} P_i(2\pi y 2^i / 2^m),$$2 and multiplies by $$U_G(y) = \bigotimes_{i=0}^{m-1} P_i(2\pi y 2^i / 2^m),$$3 (mod $$U_G(y) = \bigotimes_{i=0}^{m-1} P_i(2\pi y 2^i / 2^m),$$4):

$$U_G(y) = \bigotimes_{i=0}^{m-1} P_i(2\pi y 2^i / 2^m),$$5

Resource count for $$U_G(y) = \bigotimes_{i=0}^{m-1} P_i(2\pi y 2^i / 2^m),$$6: | Resource | Value | |-----------------|--------| | Qubits | 24 | | H-gates | 48 | | CP-gates | 828 | | Total gates | 876 | | Circuit depth | ≈72 |

• CP count: $$U_G(y) = \bigotimes_{i=0}^{m-1} P_i(2\pi y 2^i / 2^m),$$7
• Depth: $$U_G(y) = \bigotimes_{i=0}^{m-1} P_i(2\pi y 2^i / 2^m),$$8 time-steps (each step: at most one H or CP per qubit)
• No ancillas are required.

5. Depth vs. Ancilla Trade-Offs and Performance Considerations

• Depth vs. Ancilla: The "ancilla-free" construction uses $$U_G(y) = \bigotimes_{i=0}^{m-1} P_i(2\pi y 2^i / 2^m),$$9 main qubits and achieves depth $e^{2\pi i y k/2^m}$0. With even 4 extra qubits, certain computations (e.g., multi-controlled monomials) could be isolated into ancillas, enabling CP gate parallelization and reducing depth by up to $e^{2\pi i y k/2^m}$1.
• SWAP Elimination: Use of the reverse-QFT trick removes the need for end SWAP operations.
• Monomial Ordering: In general SBP encoding, monomial sequencing and CP gate scheduling can enable further parallelism if ancillas are present. Constant multiplication requires only the standard QFT order.
• Phase Resolution: For $e^{2\pi i y k/2^m}$2, the minimum CP angle is $e^{2\pi i y k/2^m}$3 rad; feasible implementation requires hardware phase error $e^{2\pi i y k/2^m}$4 rad.
• GMS Gates: On ion-trap architectures where native XX gates are available, all 828 CP gates can be replaced with as few as 24 global Mølmer–Sørensen (GMS) pulses, reducing two-qubit depth from $e^{2\pi i y k/2^m}$5 to $e^{2\pi i y k/2^m}$6.

6. Application Scope and Integration with Higher-Level Quantum Arithmetic

Any odd constant $e^{2\pi i y k/2^m}$7 can be embedded via the same phase-cascade construction, yielding a generic, compact 24-qubit multiplier. Integration with signed and mono-quantum floating-point representations is immediate: reinterpret the 24 qubits as two's-complement mantissas, and manage the exponent classically. This allows seamless extension to quantum algorithms requiring mixed integer and floating-point arithmetic, maintaining circuit depth at the 24-qubit level for in-place modular multiplication, regardless of signedness or floating-point encoding (Seidel et al., 2021).