2
to achieve a fully local cancellation of singularities [
38
–
44
].
Besides the possibility of a local regularization, including
the simultaneous cancellation of infrared and ultraviolet
singularities, the LTD formalism fully exploits causal-
ity in QFT. A recent reformulation [
45
–
57
] showed that
multiloop scattering amplitudes and Feynman integrals
can be represented in terms of a subset of cut diagrams
that generalize the well-known Cutkosky’s rules [
58
]. In
particular, a manifestly causal representation can be di-
rectly obtained by decomposing the original Feynman
diagrams into binary connected partitions in the equiva-
lence class of topologies defined by collapsing propagators
into edges (or multi-edges) [
51
,
59
]. As a result, a well
defined geometrical algorithm is available and a strong
connection between causality and directed acyclic graphs
can be established [60].
The other challenge is related to an efficient calcula-
tion of Feynman integrals, overcoming current hardware
limitations. In this direction, the development of novel
strategies for classically hard problems based on quantum
algorithms (QAs) is gaining momentum across different
areas. For instance, there are several ideas that exploit
the potential speed-up of quantum computers, such as
database querying through Grover’s algorithm [
61
], the
famous Shor’s algorithm for factorization of large inte-
gers [
62
] or Hamiltonian minimization through quantum
annealing [
63
]. In the context of particle physics, QAs are
often applied to solve problems related to lattice gauge
theories [
64
–
69
]. Recent applications for high-energy col-
liders include jet identification and clustering [
70
–
76
],
determination of parton densities (PDFs) [
77
], simulation
of parton showers [
78
,
79
], anomaly detection [
80
], and
integration of elementary particle processes [
81
]. This list
is rapidly growing, since QAs are suitable for several uses,
especially those involving minimization problems.
With this panorama in mind, the purpose of this article
is to explore the application of QAs for unveiling the
causal structure of multiloop scattering amplitudes and
Feynman integrals, or equivalently acyclic configurations
of directed graphs. Our strategy consists in exploiting
the properties of the adjacency matrix in graph theory
to build a Hamiltonian which weights the cost of differ-
ent momentum flow configurations. Explicitly, causal
configurations are those with minimum energy, so we se-
lect them by identifying the minima of the Hamiltonian.
This identification is implemented through a Variational
Quantum Eigensolver (VQE) [
82
–
84
], namely a hybrid
quantum-classical algorithm that seeks the ground state
of a given operator.
The classical problem of identifying or counting directed
acyclic graphs is #P-hard, see Ref. [
85
]. Explicitly, given
a graph
G
= (
V, E
) with
V
vertices and
E
edges, there
are 2
|E|
possible directed graphs that must be checked
for cycles. Efficient classical algorithms have been pro-
posed in the literature: e.g., Ref. [
86
] gives an example
of a Fixed Parameter Tractable algorithm, whose scaling
depends on a single structural parameter of the graph,
see also references therein. This algorithm consists of a
Binary Decision Diagram algorithm [
87
], which performs
asymptotically in
O
(2
p2
w/4
) running time per solution,
with the path-width
pw
always smaller than the input size
of the graph, becoming closer to it for dense graphs (i.e.,
maximally connected graphs). Instead, the motivation of
our paper is to encode the complexity of the graph by
means of a Hamiltonian, i.e. a cost function to be mini-
mized in order to find the directed acyclic configurations.
Since VQE is expected to offer a speed-up in minimiza-
tion problems compared to purely classical algorithms,
we explore up to what extent this speed-up remains in
the detection of directed acyclic graphs.
The outline of this paper is the following. In Sec. II,
we present a brief introduction to LTD, and we offer a de-
scription of its manifestly causal representation. Then, in
Sec. III, we discuss the geometrical aspects of the causal
configurations, presenting explicit examples in App. A.
After that, we introduce the Hamiltonian approach in
Sec. IV, putting special emphasis in the connection with
the adjacency matrix of directed acyclic graphs. We of-
fer a definition of the loop Hamiltonian and its classical
reconstruction algorithm in Secs. IV A and IV B, respec-
tively. We discuss subtleties of the encoding of vertex
registers in App. B. Then, we discuss the implementation
of the VQE in Sec. V, presenting an explicit example
for a representative two-loop topology in Sec. V A. The
full list of Hamiltonians for the topologies studied in this
article is given in App. C. Right after in Sec. V B, we
discuss an improved strategy based on multiple runs of
the VQE to collect, step by step, all the possible causal
solutions. In Sec. VI, we carefully compare the VQE
approach with the Grover’s based algorithm described in
Ref. [
60
], paying attention to the resources required for
a successful implementation in (real) quantum devices.
Finally, conclusions and further research directions are
presented in Sec. VII.
II. LOOP-TREE DUALITY AND CAUSALITY
To reach highly precise theoretical predictions, it is
necessary to deal with multiloop scattering amplitudes
and the corresponding Feynman integrals. In the Feyn-
man representation, the most general
L
-loop scattering
amplitude with Pexternal particles is given by
A(L)
F=Zℓ1...ℓL
N{ℓs}L,{pj}Pn
Y
i=1
GF(qi),(1)
where
qi
with
i∈ {
1
, . . . , n}
are the momenta flowing
through each Feynman propagator,
GF
(
qi
) = (
q2
i−m2
i
+
ı
0)
−1
, and
N
represents a numerator, whose specific form
depends on the topologies of the different diagrams, the
interaction vertices and the nature of the particles that
propagate inside the loops. Regarding the momenta of
the internal particles, they are linear combinations of
the primitive loop momenta associated to the integra-
tion variables in the loop,
ℓs
with
s∈ {
1
, . . . , L}
), and